From 6b01ee0b8ea7ecad03d03c09319b61d32f5921bb Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Sat, 9 Jul 2022 21:09:51 -0400 Subject: [PATCH 01/20] Hyperparameter optimization --- cleanrl_utils/tuner.py | 113 +++++++++++++++++++++++++++++++++++++++++ tuner_example.py | 29 +++++++++++ 2 files changed, 142 insertions(+) create mode 100644 cleanrl_utils/tuner.py create mode 100644 tuner_example.py diff --git a/cleanrl_utils/tuner.py b/cleanrl_utils/tuner.py new file mode 100644 index 000000000..496fa73bc --- /dev/null +++ b/cleanrl_utils/tuner.py @@ -0,0 +1,113 @@ +import argparse +from distutils.util import strtobool +from encodings import normalize_encoding +import json +from locale import normalize +import os +import runpy +import time +from typing import Callable +import numpy as np +import sys +import copy +from rich import print +import optuna +from tensorboard.backend.event_processing import event_accumulator + + +class HiddenPrints: + def __enter__(self): + self._original_stdout = sys.stdout + sys.stdout = open(os.devnull, 'w') + + def __exit__(self, exc_type, exc_val, exc_tb): + sys.stdout.close() + sys.stdout = self._original_stdout + +class Tuner: + def __init__( + self, + script: str, + metric: str, + target_scores: dict[str, list[int]], + params_fn: Callable[[optuna.Trial], dict], + direction: str = "maximize", + metric_last_n_average_window: int = 50, + pruner: optuna.pruners.BasePruner = None, + storage: str = "sqlite:///cleanrl_hpopt.db", + study_name: str = "", + wandb_kwargs: dict[str, any] = {}, + ) -> None: + self.script = script + self.metric = metric + self.target_scores = target_scores + self.params_fn = params_fn + self.direction = direction + self.metric_last_n_average_window = metric_last_n_average_window + self.pruner = pruner + self.storage = storage + self.study_name = study_name + if len(self.study_name) == 0: + self.study_name = f"tuner_{int(time.time())}" + self.wandb_kwargs = wandb_kwargs + + def tune(self, num_trials: int, num_seeds: int) -> None: + def objective(trial: optuna.Trial): + params = self.params_fn(trial) + run = None + if len(self.wandb_kwargs.keys()) > 0: + import wandb + + run = wandb.init( + **self.wandb_kwargs, + config=params, + name=f"{self.study_name}_{trial.number}", + group=self.study_name, + save_code=True, + reinit=True, + ) + + algo_command = [f"--{key}={value}" for key, value in params.items()] + normalized_scores = [] + for seed in range(num_seeds): + for env_id in self.target_scores.keys(): + sys.argv = algo_command + [f"--env-id={env_id}", f"--seed={seed}", "--track=False"] + with HiddenPrints(): + experiment = runpy.run_path(path_name=self.script, run_name="__main__") + + # read metric from tensorboard + ea = event_accumulator.EventAccumulator( + f"runs/{experiment['run_name']}", + ) + ea.Reload() + metric_values = [scalar_event.value for scalar_event in ea.Scalars(self.metric)[-self.metric_last_n_average_window:]] + print(f"The average episodic return on {env_id} is {np.average(metric_values)} averaged over the last {self.metric_last_n_average_window} episodes.") + normalized_scores += [(np.average(metric_values) - self.target_scores[env_id][0]) / (self.target_scores[env_id][1] - self.target_scores[env_id][0])] + if run: + run.log({f"{env_id}_return": np.average(metric_values)}) + print(f"The normalized score is {np.average(normalized_scores)} with num_seeds={seed}") + trial.report(np.average(normalized_scores), step=seed) + if trial.should_prune(): + raise optuna.TrialPruned() + if run: + run.log({"normalized_scores": np.average(normalized_scores)}) + + return np.average(normalized_scores) + + study = optuna.create_study( + study_name=self.study_name, + direction=self.direction, + storage=self.storage, + pruner=self.pruner, + ) + print("==========================================================================================") + print("run another tuner with the following command:") + print(f"python -m cleanrl_utils.tuner --study-name {self.study_name}") + print("==========================================================================================") + study.optimize( + objective, + n_trials=num_trials, + ) + print(f"The best trial obtains a normalized score of {study.best_trial.value}", study.best_trial.params) + return study.best_trial + diff --git a/tuner_example.py b/tuner_example.py new file mode 100644 index 000000000..88edd17b6 --- /dev/null +++ b/tuner_example.py @@ -0,0 +1,29 @@ +import optuna +from cleanrl_utils.tuner import Tuner + +tuner = Tuner( + script="cleanrl/ppo.py", + metric="charts/episodic_return", + metric_last_n_average_window=50, + direction="maximize", + target_scores={ + "CartPole-v1": [0, 500], + "Acrobot-v1": [-500, 0], + }, + params_fn=lambda trial: { + "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), + "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), + "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), + "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), + "total-timesteps": 10000, + "num-envs": 16, + }, + pruner=optuna.pruners.MedianPruner(n_startup_trials=5), + wandb_kwargs={"project": "cleanrl"}, +) +tuner.tune( + num_trials=10, + num_seeds=3, +) From 72ac02b44db41719aafe79b6e7a97ff2500a3d69 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Sat, 9 Jul 2022 21:57:00 -0400 Subject: [PATCH 02/20] add gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 21d3acc8c..913601cfc 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +cleanrl_hpopt.db debug.sh.docker.sh docker_cache rl-video-*.mp4 From 161b16547be9d47ad1ef1930f4751f337f3249c0 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Sat, 9 Jul 2022 21:58:42 -0400 Subject: [PATCH 03/20] pre-commit --- cleanrl_utils/tuner.py | 36 +++++++++++++++++++----------------- tuner_example.py | 3 ++- 2 files changed, 21 insertions(+), 18 deletions(-) diff --git a/cleanrl_utils/tuner.py b/cleanrl_utils/tuner.py index 496fa73bc..75e08f000 100644 --- a/cleanrl_utils/tuner.py +++ b/cleanrl_utils/tuner.py @@ -1,32 +1,28 @@ -import argparse -from distutils.util import strtobool -from encodings import normalize_encoding -import json -from locale import normalize import os import runpy +import sys import time from typing import Callable + import numpy as np -import sys -import copy -from rich import print import optuna +from rich import print from tensorboard.backend.event_processing import event_accumulator class HiddenPrints: def __enter__(self): self._original_stdout = sys.stdout - sys.stdout = open(os.devnull, 'w') + sys.stdout = open(os.devnull, "w") def __exit__(self, exc_type, exc_val, exc_tb): sys.stdout.close() sys.stdout = self._original_stdout + class Tuner: def __init__( - self, + self, script: str, metric: str, target_scores: dict[str, list[int]], @@ -50,7 +46,7 @@ def __init__( if len(self.study_name) == 0: self.study_name = f"tuner_{int(time.time())}" self.wandb_kwargs = wandb_kwargs - + def tune(self, num_trials: int, num_seeds: int) -> None: def objective(trial: optuna.Trial): params = self.params_fn(trial) @@ -80,9 +76,16 @@ def objective(trial: optuna.Trial): f"runs/{experiment['run_name']}", ) ea.Reload() - metric_values = [scalar_event.value for scalar_event in ea.Scalars(self.metric)[-self.metric_last_n_average_window:]] - print(f"The average episodic return on {env_id} is {np.average(metric_values)} averaged over the last {self.metric_last_n_average_window} episodes.") - normalized_scores += [(np.average(metric_values) - self.target_scores[env_id][0]) / (self.target_scores[env_id][1] - self.target_scores[env_id][0])] + metric_values = [ + scalar_event.value for scalar_event in ea.Scalars(self.metric)[-self.metric_last_n_average_window :] + ] + print( + f"The average episodic return on {env_id} is {np.average(metric_values)} averaged over the last {self.metric_last_n_average_window} episodes." + ) + normalized_scores += [ + (np.average(metric_values) - self.target_scores[env_id][0]) + / (self.target_scores[env_id][1] - self.target_scores[env_id][0]) + ] if run: run.log({f"{env_id}_return": np.average(metric_values)}) print(f"The normalized score is {np.average(normalized_scores)} with num_seeds={seed}") @@ -91,7 +94,7 @@ def objective(trial: optuna.Trial): raise optuna.TrialPruned() if run: run.log({"normalized_scores": np.average(normalized_scores)}) - + return np.average(normalized_scores) study = optuna.create_study( @@ -108,6 +111,5 @@ def objective(trial: optuna.Trial): objective, n_trials=num_trials, ) - print(f"The best trial obtains a normalized score of {study.best_trial.value}", study.best_trial.params) + print(f"The best trial obtains a normalized score of {study.best_trial.value}", study.best_trial.params) return study.best_trial - diff --git a/tuner_example.py b/tuner_example.py index 88edd17b6..c70998336 100644 --- a/tuner_example.py +++ b/tuner_example.py @@ -1,6 +1,7 @@ import optuna + from cleanrl_utils.tuner import Tuner - + tuner = Tuner( script="cleanrl/ppo.py", metric="charts/episodic_return", From 9a0355b33ceb1e1c13f46c9e804400655b9ec5c5 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Tue, 12 Jul 2022 09:45:55 -0400 Subject: [PATCH 04/20] quick refactor --- cleanrl_utils/tuner.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/cleanrl_utils/tuner.py b/cleanrl_utils/tuner.py index 75e08f000..600fec029 100644 --- a/cleanrl_utils/tuner.py +++ b/cleanrl_utils/tuner.py @@ -8,6 +8,7 @@ import optuna from rich import print from tensorboard.backend.event_processing import event_accumulator +import wandb class HiddenPrints: @@ -52,8 +53,6 @@ def objective(trial: optuna.Trial): params = self.params_fn(trial) run = None if len(self.wandb_kwargs.keys()) > 0: - import wandb - run = wandb.init( **self.wandb_kwargs, config=params, @@ -91,10 +90,12 @@ def objective(trial: optuna.Trial): print(f"The normalized score is {np.average(normalized_scores)} with num_seeds={seed}") trial.report(np.average(normalized_scores), step=seed) if trial.should_prune(): + run.finish(quiet=True) raise optuna.TrialPruned() if run: run.log({"normalized_scores": np.average(normalized_scores)}) - + + run.finish(quiet=True) return np.average(normalized_scores) study = optuna.create_study( From c9cc5f160e15d83fe0e3475fc1c295cb2db04bbe Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Fri, 15 Jul 2022 23:31:14 -0400 Subject: [PATCH 05/20] Add docs --- docs/advanced/hyperparameter-tuning.md | 211 +++++++++++++++++++++++++ mkdocs.yml | 5 +- 2 files changed, 214 insertions(+), 2 deletions(-) create mode 100644 docs/advanced/hyperparameter-tuning.md diff --git a/docs/advanced/hyperparameter-tuning.md b/docs/advanced/hyperparameter-tuning.md new file mode 100644 index 000000000..fa40b7d44 --- /dev/null +++ b/docs/advanced/hyperparameter-tuning.md @@ -0,0 +1,211 @@ +# Hyperparameter Tuning + +CleanRL comes with a simple and practical hyperparameter tuning utility `Tuner`. + +## Get started + +Create the following file: + +```python title="tuner_example.py" +from cleanrl_utils.tuner import Tuner +tuner = Tuner( + script="cleanrl/ppo.py", + metric="charts/episodic_return", + metric_last_n_average_window=50, + direction="maximize", + target_scores={ + "CartPole-v1": [0, 500], + }, + params_fn=lambda trial: { + "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), + "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), + "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), + "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), + "total-timesteps": 10000, + "num-envs": 4, + }, +) +tuner.tune( + num_trials=100, + num_seeds=3, +) +``` + +Then you can run the tuner with + +```bash +poetry install optuna +python tuner_example.py +``` + +Here is what happened: + +1. The `tuner_example.py` launches `num_trials=100` *trials* to find the best single set of hyperparameters for `CartPole-v1` in `script="cleanrl/ppo.py"`. +1. Each *trial* uses a set of hyperparameters sampled from the `params_fn` to run `num_seeds=3` *experiments* with different random seeds, mitigating the impact of randomness on the results. + * In each *experiment*, `tuner_example.py` averages the last `metric_last_n_average_window=50` reported `metric="charts/episodic_return"` to a number $x_i$ and calculate a normalized score $z_i = (x_i - 0) / (500 - 0)$ according to `target_scores`. +1. Each *trial* then averages the normalized scores $z_i$ of the three *experiments* to a number $z$ and the tuner optimizes $z$ according `direction="maximize"`. + + +## Visualization + +Running `python tuner_example.py` will create a sqlite database containing all of the hyperparameter trials in `./cleanrl_hpopt.db`. We can use [optuna-dashboard](https://github.com/optuna/optuna-dashboard) to visualize the process. + +```bash +poetry run optuna-dashboard sqlite:///cleanrl_hpopt.db +``` + +You can use a different database by passing `Tuner(..., storage="mysql://root@localhost/example")`, for example. + + +## Work w/ multiple environments + +`Tuner` supports finding a set of hyper parameters of that works well against multiple environments by extending `target_scores`. In the following example, each trial uses a set of hyperparameters to run experiments with 3 random seeds for each environment in `["CartPole-v1","Acrobot-v1"]`, totalling `2*3=6` experiments per trial. + + +```python title="tuner_example_multi_env.py" hl_lines="8 9" +from cleanrl_utils.tuner import Tuner +tuner = Tuner( + script="cleanrl/ppo.py", + metric="charts/episodic_return", + metric_last_n_average_window=50, + direction="maximize", + target_scores={ + "CartPole-v1": [0, 500], + "Acrobot-v1": [-500, 0], + }, + params_fn=lambda trial: { + "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), + "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), + "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), + "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), + "total-timesteps": 10000, + "num-envs": 16, + }, +) +tuner.tune( + num_trials=100, + num_seeds=3, +) +``` + + +???+ info + + When optimizing Atari games, you can use `target_scores` as the human normalized scores in (Mnih et al., 2015, Extended Data Table 2)[^1], such as + + ```python + tuner = Tuner( + script="cleanrl/ppo_atari.py", + metric="charts/episodic_return", + metric_last_n_average_window=50, + direction="maximize", + target_scores={ + "Alien-v5": [227.8, 6875], + "Amidar-v5": [5.8, 1676], + 'Assault-v5': (222.4, 1496), + 'Asterix-v5': (210.0, 8503), + 'Asteroids-v5': (719.1, 13157), + ... + }, + ... + ) + ``` + + +## Work w/ pruners + +You can use `Tuner` with any pruner from `optuna` to prune less promising experiments: + +```python title="tuner_example_pruner.py" hl_lines="1 22" +import optuna +from cleanrl_utils.tuner import Tuner +tuner = Tuner( + script="cleanrl/ppo.py", + metric="charts/episodic_return", + metric_last_n_average_window=50, + direction="maximize", + target_scores={ + "CartPole-v1": [0, 500], + "Acrobot-v1": [-500, 0], + }, + params_fn=lambda trial: { + "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), + "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), + "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), + "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), + "total-timesteps": 10000, + "num-envs": 16, + }, + pruner=optuna.pruners.MedianPruner(n_startup_trials=5), +) +tuner.tune( + num_trials=100, + num_seeds=3, +) +``` + + +## Track experiments w/ Weights and Biases + +The `Tuner` can track all the experiments into [Weights and Biases](https://wandb.ai) to help you visualize the progress of the tuning. + + +```python title="tuner_example.py" hl_lines="23" +import optuna +from cleanrl_utils.tuner import Tuner +tuner = Tuner( + script="cleanrl/ppo.py", + metric="charts/episodic_return", + metric_last_n_average_window=50, + direction="maximize", + target_scores={ + "CartPole-v1": [0, 500], + "Acrobot-v1": [-500, 0], + }, + params_fn=lambda trial: { + "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), + "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), + "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), + "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), + "total-timesteps": 10000, + "num-envs": 16, + }, + pruner=optuna.pruners.MedianPruner(n_startup_trials=5), + wandb_kwargs={"project": "cleanrl"}, +) +tuner.tune( + num_trials=100, + num_seeds=3, +) +``` + + + + + +[^1]:Mnih, V., Kavukcuoglu, K., Silver, D. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015). https://doi.org/10.1038/nature14236 \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 934626b3a..5c5d969c3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -85,12 +85,13 @@ nav: - rl-algorithms/sac.md - rl-algorithms/td3.md - rl-algorithms/ppg.md + - Advanced: + - advanced/hyperparameter-tuning.md + - advanced/resume-training.md # - Open RL Benchmark: open-rl-benchmark.md - Community: - contribution.md - made-with-cleanrl.md - - Advanced: - - advanced/resume-training.md - Cloud Integration: - cloud/installation.md - cloud/submit-experiments.md From 3ac87956265d062685b21705f99cff344743fe2b Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Mon, 22 Aug 2022 18:01:17 -0400 Subject: [PATCH 06/20] pre-commit --- cleanrl_utils/tuner.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cleanrl_utils/tuner.py b/cleanrl_utils/tuner.py index 600fec029..1e49cab83 100644 --- a/cleanrl_utils/tuner.py +++ b/cleanrl_utils/tuner.py @@ -6,9 +6,9 @@ import numpy as np import optuna +import wandb from rich import print from tensorboard.backend.event_processing import event_accumulator -import wandb class HiddenPrints: @@ -94,7 +94,7 @@ def objective(trial: optuna.Trial): raise optuna.TrialPruned() if run: run.log({"normalized_scores": np.average(normalized_scores)}) - + run.finish(quiet=True) return np.average(normalized_scores) From 444cf67ec64954c7b953ed9b393a5cb60be5560a Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Mon, 22 Aug 2022 18:06:23 -0400 Subject: [PATCH 07/20] clarify docs --- docs/advanced/hyperparameter-tuning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/advanced/hyperparameter-tuning.md b/docs/advanced/hyperparameter-tuning.md index fa40b7d44..09fa3238e 100644 --- a/docs/advanced/hyperparameter-tuning.md +++ b/docs/advanced/hyperparameter-tuning.md @@ -44,7 +44,7 @@ Here is what happened: 1. The `tuner_example.py` launches `num_trials=100` *trials* to find the best single set of hyperparameters for `CartPole-v1` in `script="cleanrl/ppo.py"`. 1. Each *trial* uses a set of hyperparameters sampled from the `params_fn` to run `num_seeds=3` *experiments* with different random seeds, mitigating the impact of randomness on the results. - * In each *experiment*, `tuner_example.py` averages the last `metric_last_n_average_window=50` reported `metric="charts/episodic_return"` to a number $x_i$ and calculate a normalized score $z_i = (x_i - 0) / (500 - 0)$ according to `target_scores`. + * In each *experiment*, `tuner_example.py` averages the last `metric_last_n_average_window=50` reported `metric="charts/episodic_return"` to a number $x_i$ and calculate a normalized score $z_i = (x_i - 0) / (500 - 0)$ according to `target_scores`, where $0$ and $500$ are the minimum and maximum values of the `metric` for `CartPole-v1`. 1. Each *trial* then averages the normalized scores $z_i$ of the three *experiments* to a number $z$ and the tuner optimizes $z$ according `direction="maximize"`. From 86e8ef1ab869f939950bcd90ed4a5cb0d0e6995e Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Mon, 22 Aug 2022 18:15:47 -0400 Subject: [PATCH 08/20] update docs --- docs/advanced/hyperparameter-tuning.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/advanced/hyperparameter-tuning.md b/docs/advanced/hyperparameter-tuning.md index 09fa3238e..c3c8f496f 100644 --- a/docs/advanced/hyperparameter-tuning.md +++ b/docs/advanced/hyperparameter-tuning.md @@ -116,11 +116,11 @@ tuner.tune( ``` -## Work w/ pruners +## Work w/ pruners and samplers -You can use `Tuner` with any pruner from `optuna` to prune less promising experiments: +You can use `Tuner` with any [pruner](https://optuna.readthedocs.io/en/stable/reference/pruners.html) from `optuna` to prune less promising experiments or [samplers](https://optuna.readthedocs.io/en/stable/reference/samplers.html) to sample new hyperparameters. If you don't specify them explicitly, the script will use the [default ones](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.create_study.html). -```python title="tuner_example_pruner.py" hl_lines="1 22" +```python title="tuner_example_pruner.py" hl_lines="1 22 23" import optuna from cleanrl_utils.tuner import Tuner tuner = Tuner( @@ -143,6 +143,7 @@ tuner = Tuner( "num-envs": 16, }, pruner=optuna.pruners.MedianPruner(n_startup_trials=5), + sampler=optuna.samplers.TPESampler(), ) tuner.tune( num_trials=100, @@ -156,7 +157,7 @@ tuner.tune( The `Tuner` can track all the experiments into [Weights and Biases](https://wandb.ai) to help you visualize the progress of the tuning. -```python title="tuner_example.py" hl_lines="23" +```python title="tuner_example.py" hl_lines="24" import optuna from cleanrl_utils.tuner import Tuner tuner = Tuner( @@ -179,6 +180,7 @@ tuner = Tuner( "num-envs": 16, }, pruner=optuna.pruners.MedianPruner(n_startup_trials=5), + sampler=optuna.samplers.TPESampler(), wandb_kwargs={"project": "cleanrl"}, ) tuner.tune( From c1ef1a0f5135048025bff777e20fa8778ecc965e Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Mon, 22 Aug 2022 22:39:36 -0400 Subject: [PATCH 09/20] push changes --- cleanrl_utils/tuner.py | 66 ++++++++++++++++++------- docs/advanced/hyperparameter-tuning.md | 34 +++++++++---- docs/advanced/optuna-dashboard.png | Bin 0 -> 901780 bytes tuner_example.py | 3 +- 4 files changed, 75 insertions(+), 28 deletions(-) create mode 100644 docs/advanced/optuna-dashboard.png diff --git a/cleanrl_utils/tuner.py b/cleanrl_utils/tuner.py index 1e49cab83..2f7e94920 100644 --- a/cleanrl_utils/tuner.py +++ b/cleanrl_utils/tuner.py @@ -2,7 +2,7 @@ import runpy import sys import time -from typing import Callable +from typing import Callable, Dict, List, Optional import numpy as np import optuna @@ -26,22 +26,42 @@ def __init__( self, script: str, metric: str, - target_scores: dict[str, list[int]], - params_fn: Callable[[optuna.Trial], dict], + target_scores: Dict[str, Optional[List[float]]], + params_fn: Callable[[optuna.Trial], Dict], direction: str = "maximize", + aggregation_type: str = "average", metric_last_n_average_window: int = 50, - pruner: optuna.pruners.BasePruner = None, + sampler: Optional[optuna.samplers.BaseSampler] = None, + pruner: Optional[optuna.pruners.BasePruner] = None, storage: str = "sqlite:///cleanrl_hpopt.db", study_name: str = "", - wandb_kwargs: dict[str, any] = {}, + wandb_kwargs: Dict[str, any] = {}, ) -> None: self.script = script self.metric = metric self.target_scores = target_scores + if len(self.target_scores) > 1: + if self.target_scores.values()[0] is None: + raise ValueError( + "If there are multiple environments, the target scores must be specified for each environment." + ) + self.params_fn = params_fn self.direction = direction + self.aggregation_type = aggregation_type + if self.aggregation_type == "average": + self.aggregation_fn = np.average + elif self.aggregation_type == "median": + self.aggregation_fn = np.median + elif self.aggregation_type == "max": + self.aggregation_fn = np.max + elif self.aggregation_type == "min": + self.aggregation_fn = np.min + else: + raise ValueError(f"Unknown aggregation type {self.aggregation_type}") self.metric_last_n_average_window = metric_last_n_average_window self.pruner = pruner + self.sampler = sampler self.storage = storage self.study_name = study_name if len(self.study_name) == 0: @@ -63,8 +83,9 @@ def objective(trial: optuna.Trial): ) algo_command = [f"--{key}={value}" for key, value in params.items()] - normalized_scores = [] + normalized_scoress = [] for seed in range(num_seeds): + normalized_scores = [] for env_id in self.target_scores.keys(): sys.argv = algo_command + [f"--env-id={env_id}", f"--seed={seed}", "--track=False"] with HiddenPrints(): @@ -81,28 +102,39 @@ def objective(trial: optuna.Trial): print( f"The average episodic return on {env_id} is {np.average(metric_values)} averaged over the last {self.metric_last_n_average_window} episodes." ) - normalized_scores += [ - (np.average(metric_values) - self.target_scores[env_id][0]) - / (self.target_scores[env_id][1] - self.target_scores[env_id][0]) - ] + if self.target_scores[env_id] is not None: + normalized_scores += [ + (np.average(metric_values) - self.target_scores[env_id][0]) + / (self.target_scores[env_id][1] - self.target_scores[env_id][0]) + ] + else: + normalized_scores += [np.average(metric_values)] if run: run.log({f"{env_id}_return": np.average(metric_values)}) - print(f"The normalized score is {np.average(normalized_scores)} with num_seeds={seed}") - trial.report(np.average(normalized_scores), step=seed) + + normalized_scoress += [normalized_scores] + aggregated_normalized_score = self.aggregation_fn(normalized_scores) + print(f"The {self.aggregation_type} normalized score is {aggregated_normalized_score} with num_seeds={seed}") + trial.report(aggregated_normalized_score, step=seed) + if run: + run.log({"aggregated_normalized_score": aggregated_normalized_score}) if trial.should_prune(): - run.finish(quiet=True) + if run: + run.finish(quiet=True) raise optuna.TrialPruned() - if run: - run.log({"normalized_scores": np.average(normalized_scores)}) - run.finish(quiet=True) - return np.average(normalized_scores) + if run: + run.finish(quiet=True) + return np.average( + self.aggregation_fn(normalized_scoress, axis=1) + ) # we alaways return the average of the aggregated normalized scores study = optuna.create_study( study_name=self.study_name, direction=self.direction, storage=self.storage, pruner=self.pruner, + sampler=self.sampler, ) print("==========================================================================================") print("run another tuner with the following command:") diff --git a/docs/advanced/hyperparameter-tuning.md b/docs/advanced/hyperparameter-tuning.md index c3c8f496f..71f7376bd 100644 --- a/docs/advanced/hyperparameter-tuning.md +++ b/docs/advanced/hyperparameter-tuning.md @@ -13,8 +13,9 @@ tuner = Tuner( metric="charts/episodic_return", metric_last_n_average_window=50, direction="maximize", + aggregation_type="average", target_scores={ - "CartPole-v1": [0, 500], + "CartPole-v1": None, }, params_fn=lambda trial: { "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), @@ -36,16 +37,16 @@ tuner.tune( Then you can run the tuner with ```bash -poetry install optuna +poetry install -E optuna python tuner_example.py ``` Here is what happened: 1. The `tuner_example.py` launches `num_trials=100` *trials* to find the best single set of hyperparameters for `CartPole-v1` in `script="cleanrl/ppo.py"`. -1. Each *trial* uses a set of hyperparameters sampled from the `params_fn` to run `num_seeds=3` *experiments* with different random seeds, mitigating the impact of randomness on the results. - * In each *experiment*, `tuner_example.py` averages the last `metric_last_n_average_window=50` reported `metric="charts/episodic_return"` to a number $x_i$ and calculate a normalized score $z_i = (x_i - 0) / (500 - 0)$ according to `target_scores`, where $0$ and $500$ are the minimum and maximum values of the `metric` for `CartPole-v1`. -1. Each *trial* then averages the normalized scores $z_i$ of the three *experiments* to a number $z$ and the tuner optimizes $z$ according `direction="maximize"`. +1. Each *trial* samples a set of hyperparameters from the `params_fn` to run `num_seeds=3` *experiments* with different random seeds, mitigating the impact of randomness on the results. + * In each *experiment*, `tuner_example.py` averages the last `metric_last_n_average_window=50` reported `metric="charts/episodic_return"` to a number $x_i$ and calculate a normalized score $z_i$ according to the `target_scores`. We will talk more about `target_scores` which is useful when working with multiple environments in the next section, but in this section we just set `None` as the target score for `CartPole-v1` — this means we will set the normalized score $z_i = x_i$. +1. Each *trial* then averages the normalized scores $z_i$ of the `num_seeds=3` *experiments* to a number $z$ and the tuner optimizes $z$ according `direction="maximize"`. ## Visualization @@ -59,18 +60,23 @@ poetry run optuna-dashboard sqlite:///cleanrl_hpopt.db You can use a different database by passing `Tuner(..., storage="mysql://root@localhost/example")`, for example. + + ## Work w/ multiple environments -`Tuner` supports finding a set of hyper parameters of that works well against multiple environments by extending `target_scores`. In the following example, each trial uses a set of hyperparameters to run experiments with 3 random seeds for each environment in `["CartPole-v1","Acrobot-v1"]`, totalling `2*3=6` experiments per trial. +Often it is useful to find a single set of hyper parameters that work well with multiple environments, but this is challenging **because each environment may have different reward scales**, so we need to do normalization. + +This is where `target_scores` comes in. We can use it to specify an upper and lower threshold of rewards for each environment (they don't have to be exact boundaries and can be just ballpark estimates). For example, if we want to find a set of hyper parameters that work well with `CartPole-v1` and `Acrobot-v1`, we can set the `target_scores` and `aggregation_type` as follows: -```python title="tuner_example_multi_env.py" hl_lines="8 9" +```python title="tuner_example_multi_env.py" hl_lines="7-11" from cleanrl_utils.tuner import Tuner tuner = Tuner( script="cleanrl/ppo.py", metric="charts/episodic_return", metric_last_n_average_window=50, direction="maximize", + aggregation_type="average", target_scores={ "CartPole-v1": [0, 500], "Acrobot-v1": [-500, 0], @@ -92,10 +98,15 @@ tuner.tune( ) ``` +Then each experiment will calculate the normalized scores $z_{i_0} = (x_{i_0} - 0) / (500 - 0), z_{i_1} = (x_{i_1} - -500) / (0 - -500)$ and the tuner will `aggregation_type="average"` the two scores to get the normalized score $z_i = (z_{i_0} + z_{i_1}) / 2$. + +Note that we are using 3 random seeds for each environment in `["CartPole-v1","Acrobot-v1"]`, totalling `2*3=6` experiments per trial. + + ???+ info - When optimizing Atari games, you can use `target_scores` as the human normalized scores in (Mnih et al., 2015, Extended Data Table 2)[^1], such as + When optimizing Atari games, you can put the human normalized scores in `target_scores` (Mnih et al., 2015, Extended Data Table 2)[^1], as done in the following example. The first number for each environment is the score obtained by random play and the second number is the score obtained by professional game testers. ```python tuner = Tuner( @@ -103,6 +114,7 @@ tuner.tune( metric="charts/episodic_return", metric_last_n_average_window=50, direction="maximize", + aggregation_type="average", target_scores={ "Alien-v5": [227.8, 6875], "Amidar-v5": [5.8, 1676], @@ -120,7 +132,7 @@ tuner.tune( You can use `Tuner` with any [pruner](https://optuna.readthedocs.io/en/stable/reference/pruners.html) from `optuna` to prune less promising experiments or [samplers](https://optuna.readthedocs.io/en/stable/reference/samplers.html) to sample new hyperparameters. If you don't specify them explicitly, the script will use the [default ones](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.create_study.html). -```python title="tuner_example_pruner.py" hl_lines="1 22 23" +```python title="tuner_example_pruner.py" hl_lines="1 23 24" import optuna from cleanrl_utils.tuner import Tuner tuner = Tuner( @@ -128,6 +140,7 @@ tuner = Tuner( metric="charts/episodic_return", metric_last_n_average_window=50, direction="maximize", + aggregation_type="average", target_scores={ "CartPole-v1": [0, 500], "Acrobot-v1": [-500, 0], @@ -157,7 +170,7 @@ tuner.tune( The `Tuner` can track all the experiments into [Weights and Biases](https://wandb.ai) to help you visualize the progress of the tuning. -```python title="tuner_example.py" hl_lines="24" +```python title="tuner_example.py" hl_lines="25" import optuna from cleanrl_utils.tuner import Tuner tuner = Tuner( @@ -165,6 +178,7 @@ tuner = Tuner( metric="charts/episodic_return", metric_last_n_average_window=50, direction="maximize", + aggregation_type="average", target_scores={ "CartPole-v1": [0, 500], "Acrobot-v1": [-500, 0], diff --git a/docs/advanced/optuna-dashboard.png b/docs/advanced/optuna-dashboard.png new file mode 100644 index 0000000000000000000000000000000000000000..277e9a270a7382d285dce586661ca57d1a2c58eb GIT binary patch literal 901780 zcmeFZXIN9+);5ZYqK^ue=TQ+fAPOP~0t(U+5fv2>1Qn4IsS#;H=#WH2MFa`bkrELB z6$I&0Bhp(UHT2L!4-iNpWt|n>d%ycT->-9B=luFk))iKpbFMY#9CM6&++&0=<6C-r z|2q1YfPlc>8`rOx2ng&v34XP9?f|cZel9Z*5cn(8NmtkShOVxJv8TJElZ%6Z!1b`W zcp+ocxkFEZz;3Zq!PjNhrLXVbDRFB3S7ykYsOKkRkC+-4$c4*&KXm2FCG*a^`?s5A zzS>*x&_eLex7~%OKP}kSzHF`hvCoIm5$HZzP9xJgT3ALb3NLA!z>D^`*OhH92o%

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-0400 Subject: [PATCH 10/20] push changes --- docs/advanced/hyperparameter-tuning.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/advanced/hyperparameter-tuning.md b/docs/advanced/hyperparameter-tuning.md index 71f7376bd..e94ad5632 100644 --- a/docs/advanced/hyperparameter-tuning.md +++ b/docs/advanced/hyperparameter-tuning.md @@ -57,9 +57,8 @@ Running `python tuner_example.py` will create a sqlite database containing all o poetry run optuna-dashboard sqlite:///cleanrl_hpopt.db ``` -You can use a different database by passing `Tuner(..., storage="mysql://root@localhost/example")`, for example. - - +You can use a different database by passing `Tuner(..., storage="mysql://root@localhost/example")` to use a more persistant storage. +![](./optuna-dashboard.png) ## Work w/ multiple environments From 94f830be4aefbdc7e754c85904caf3f4c1506678 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Mon, 22 Aug 2022 22:40:17 -0400 Subject: [PATCH 11/20] typo --- docs/advanced/hyperparameter-tuning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/advanced/hyperparameter-tuning.md b/docs/advanced/hyperparameter-tuning.md index e94ad5632..10ee83096 100644 --- a/docs/advanced/hyperparameter-tuning.md +++ b/docs/advanced/hyperparameter-tuning.md @@ -57,7 +57,7 @@ Running `python tuner_example.py` will create a sqlite database containing all o poetry run optuna-dashboard sqlite:///cleanrl_hpopt.db ``` -You can use a different database by passing `Tuner(..., storage="mysql://root@localhost/example")` to use a more persistant storage. +You can use a different database by passing `Tuner(..., storage="mysql://root@localhost/example")` to use a more persistent storage. ![](./optuna-dashboard.png) From be004dbf94f49a5fe2861ff8834249734f30ae15 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Mon, 22 Aug 2022 22:57:58 -0400 Subject: [PATCH 12/20] update dependencies --- poetry.lock | 889 ++++++++++++++++++++++++++++++++++++++++++++----- pyproject.toml | 4 + 2 files changed, 812 insertions(+), 81 deletions(-) diff --git a/poetry.lock b/poetry.lock index b67b3995a..9db8c79ad 100644 --- a/poetry.lock +++ b/poetry.lock @@ -25,6 +25,23 @@ numpy = "*" [package.extras] test = ["pytest", "gym"] +[[package]] +name = "alembic" +version = "1.8.1" +description = "A database migration tool for SQLAlchemy." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} +importlib-resources = {version = "*", markers = "python_version < \"3.9\""} +Mako = "*" +SQLAlchemy = ">=1.3.0" + +[package.extras] +tz = ["python-dateutil"] + [[package]] name = "antlr4-python3-runtime" version = "4.9.3" @@ -61,10 +78,18 @@ optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"] +tests_no_zope = ["cloudpickle", "pytest-mypy-plugins", "mypy", "six", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"] +tests = ["cloudpickle", "zope.interface", "pytest-mypy-plugins", "mypy", "six", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"] +docs = ["sphinx-notfound-page", "zope.interface", "sphinx", "furo"] +dev = ["cloudpickle", "pre-commit", "sphinx-notfound-page", "sphinx", "furo", "zope.interface", "pytest-mypy-plugins", "mypy", "six", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"] + +[[package]] +name = "autopage" +version = "0.5.1" +description = "A library to provide automatic paging for console output" +category = "main" +optional = true +python-versions = ">=3.6" [[package]] name = "autorom" @@ -149,6 +174,14 @@ urllib3 = ">=1.25.4,<1.27" [package.extras] crt = ["awscrt (==0.12.5)"] +[[package]] +name = "bottle" +version = "0.12.23" +description = "Fast and simple WSGI-framework for small web-applications." +category = "main" +optional = true +python-versions = "*" + [[package]] name = "cachetools" version = "5.0.0" @@ -223,6 +256,23 @@ python-versions = ">=3.6" colorama = {version = "*", markers = "platform_system == \"Windows\""} importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} +[[package]] +name = "cliff" +version = "3.10.1" +description = "Command Line Interface Formulation Framework" +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +autopage = ">=0.4.0" +cmd2 = ">=1.0.0" +pbr = ">=2.0.0,<2.1.0 || >2.1.0" +PrettyTable = ">=0.7.2" +pyparsing = ">=2.1.0" +PyYAML = ">=3.12" +stevedore = ">=2.0.1" + [[package]] name = "cloudpickle" version = "2.0.0" @@ -231,6 +281,38 @@ category = "main" optional = false python-versions = ">=3.6" +[[package]] +name = "cmaes" +version = "0.8.2" +description = "Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation for Python 3." +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +numpy = "*" + +[[package]] +name = "cmd2" +version = "2.4.2" +description = "cmd2 - quickly build feature-rich and user-friendly interactive command line applications in Python" +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +attrs = ">=16.3.0" +importlib-metadata = {version = ">=1.6.0", markers = "python_version < \"3.8\""} +pyperclip = ">=1.6" +pyreadline3 = {version = "*", markers = "sys_platform == \"win32\""} +typing-extensions = {version = "*", markers = "python_version < \"3.8\""} +wcwidth = ">=0.1.7" + +[package.extras] +dev = ["codecov", "doc8", "flake8", "invoke", "mypy (==0.902)", "nox", "pytest (>=4.6)", "pytest-cov", "pytest-mock", "sphinx", "sphinx-rtd-theme", "sphinx-autobuild", "twine (>=1.11)"] +test = ["codecov", "coverage", "pytest (>=4.6)", "pytest-cov", "pytest-mock", "gnureadline"] +validate = ["flake8", "mypy (==0.902)", "types-pkg-resources"] + [[package]] name = "colorama" version = "0.4.3" @@ -239,6 +321,20 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +[[package]] +name = "colorlog" +version = "6.6.0" +description = "Add colours to the output of Python's logging module." +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +colorama = {version = "*", markers = "sys_platform == \"win32\""} + +[package.extras] +development = ["black", "flake8", "mypy", "pytest", "types-colorama"] + [[package]] name = "commonmark" version = "0.9.1" @@ -248,7 +344,7 @@ optional = true python-versions = "*" [package.extras] -test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"] +test = ["hypothesis (==3.55.3)", "flake8 (==3.7.8)"] [[package]] name = "cycler" @@ -278,7 +374,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" wrapt = ">=1.10,<2" [package.extras] -dev = ["tox", "bump2version (<1)", "sphinx (<2)", "importlib-metadata (<3)", "importlib-resources (<4)", "configparser (<5)", "sphinxcontrib-websupport (<2)", "zipp (<2)", "PyTest (<5)", "PyTest-Cov (<2.6)", "pytest", "pytest-cov"] +dev = ["pytest-cov", "pytest", "PyTest-Cov (<2.6)", "PyTest (<5)", "zipp (<2)", "sphinxcontrib-websupport (<2)", "configparser (<5)", "importlib-resources (<4)", "importlib-metadata (<3)", "sphinx (<2)", "bump2version (<1)", "tox"] [[package]] name = "distlib" @@ -348,7 +444,7 @@ typing-extensions = "*" [[package]] name = "etils" -version = "0.6.0" +version = "0.7.1" description = "Collection of common python utils" category = "main" optional = true @@ -357,24 +453,23 @@ python-versions = ">=3.7" [package.dependencies] etils = {version = "*", extras = ["epy"], optional = true, markers = "extra == \"epath\""} importlib_resources = {version = "*", optional = true, markers = "extra == \"epath\""} -typing_extensions = {version = "*", optional = true, markers = "extra == \"epy\""} +typing_extensions = {version = "*", optional = true, markers = "extra == \"epath\""} zipp = {version = "*", optional = true, markers = "extra == \"epath\""} [package.extras] -all = ["etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils"] -array-types = ["numpy"] -dev = ["pytest", "pytest-subtests", "pytest-xdist", "pylint (>=2.6.0)", "yapf", "chex"] -ecolab = ["jupyter", "numpy", "mediapy", "etils"] -edc = ["etils"] -enp = ["numpy", "etils"] -epath = ["importlib-resources", "zipp", "etils"] -epath-no-tf = ["etils"] -epy = ["typing-extensions"] -etqdm = ["absl-py", "tqdm", "etils"] +etree-tf = ["etils", "tf-nightly"] +etree-jax = ["etils", "jax"] +etree-dm = ["etils", "dm-tree"] etree = ["etils", "etils", "etils", "etils"] -etree-dm = ["dm-tree", "etils"] -etree-jax = ["jax", "etils"] -etree-tf = ["tf-nightly", "etils"] +etqdm = ["etils", "tqdm", "absl-py"] +epy = ["typing-extensions"] +epath = ["etils", "zipp", "typing-extensions", "importlib-resources"] +enp = ["etils", "numpy"] +edc = ["etils"] +ecolab = ["etils", "etils", "mediapy", "numpy", "jupyter"] +dev = ["chex", "yapf", "pylint (>=2.6.0)", "pytest-xdist", "pytest-subtests", "pytest"] +array-types = ["etils"] +all = ["etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils"] [[package]] name = "fasteners" @@ -397,8 +492,8 @@ optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2021.8.17b43)", "sphinx (>=4.1)", "sphinx-autodoc-typehints (>=1.12)"] -testing = ["covdefaults (>=1.2.0)", "coverage (>=4)", "pytest (>=4)", "pytest-cov", "pytest-timeout (>=1.4.2)"] +testing = ["pytest-timeout (>=1.4.2)", "pytest-cov", "pytest (>=4)", "coverage (>=4)", "covdefaults (>=1.2.0)"] +docs = ["sphinx-autodoc-typehints (>=1.12)", "sphinx (>=4.1)", "furo (>=2021.8.17b43)"] [[package]] name = "flatbuffers" @@ -427,7 +522,7 @@ rich = ">=11.1,<12.0" typing-extensions = ">=4.1.1" [package.extras] -testing = ["atari-py (==0.2.5)", "clu", "gym (==0.18.3)", "jaxlib", "jraph", "ml-collections", "opencv-python", "pytest", "pytest-cov", "pytest-xdist (==1.34.0)", "pytype", "sentencepiece", "svn", "tensorflow-text (>=2.4.0)", "tensorflow-datasets", "tensorflow", "torch", "pandas"] +testing = ["pandas", "torch", "tensorflow", "tensorflow-datasets", "tensorflow-text (>=2.4.0)", "svn", "sentencepiece", "pytype", "pytest-xdist (==1.34.0)", "pytest-cov", "pytest", "opencv-python", "ml-collections", "jraph", "jaxlib", "gym (==0.18.3)", "clu", "atari-py (==0.2.5)"] [[package]] name = "fonttools" @@ -478,7 +573,7 @@ python-versions = "*" python-dateutil = ">=2.8.1" [package.extras] -dev = ["twine", "markdown", "flake8", "wheel"] +dev = ["wheel", "flake8", "markdown", "twine"] [[package]] name = "gitdb" @@ -553,6 +648,17 @@ requests-oauthlib = ">=0.7.0" [package.extras] tool = ["click (>=6.0.0)"] +[[package]] +name = "greenlet" +version = "1.1.2" +description = "Lightweight in-process concurrent programming" +category = "main" +optional = true +python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" + +[package.extras] +docs = ["sphinx"] + [[package]] name = "grpcio" version = "1.44.0" @@ -618,7 +724,7 @@ moderngl = ">=5.5.4,<6.0.0" numpy = ">=1.11.0,<2.0.0" [package.extras] -test = ["pytest (==5.2.1)", "pytest-benchmark (==3.2.2)", "gym-retro (==0.8.0)", "gym (==0.17.2)", "tensorflow (==1.15.0)", "mpi4py (==3.0.3)"] +test = ["mpi4py (==3.0.3)", "tensorflow (==1.15.0)", "gym (==0.17.2)", "gym-retro (==0.8.0)", "pytest-benchmark (==3.2.2)", "pytest (==5.2.1)"] [[package]] name = "hydra-core" @@ -703,9 +809,9 @@ typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""} zipp = ">=0.5" [package.extras] -docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"] +testing = ["importlib-resources (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-black (>=0.3.7)", "pytest-perf (>=0.9.2)", "flufl.flake8", "pyfakefs", "packaging", "pytest-enabler (>=1.0.1)", "pytest-cov", "pytest-flake8", "pytest-checkdocs (>=2.4)", "pytest (>=6)"] perf = ["ipython"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"] +docs = ["rst.linker (>=1.9)", "jaraco.packaging (>=9)", "sphinx"] [[package]] name = "importlib-resources" @@ -719,8 +825,8 @@ python-versions = ">=3.6" zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "pytest-black (>=0.3.7)", "pytest-mypy"] +testing = ["pytest-mypy", "pytest-black (>=0.3.7)", "pytest-enabler (>=1.0.1)", "pytest-cov", "pytest-flake8", "pytest-checkdocs (>=2.4)", "pytest (>=6)"] +docs = ["rst.linker (>=1.9)", "jaraco.packaging (>=8.2)", "sphinx"] [[package]] name = "iniconfig" @@ -839,6 +945,14 @@ category = "main" optional = true python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" +[[package]] +name = "joblib" +version = "1.1.0" +description = "Lightweight pipelining with Python functions" +category = "main" +optional = true +python-versions = ">=3.6" + [[package]] name = "jsonschema" version = "4.7.2" @@ -855,8 +969,8 @@ pyrsistent = ">=0.14.0,<0.17.0 || >0.17.0,<0.17.1 || >0.17.1,<0.17.2 || >0.17.2" typing-extensions = {version = "*", markers = "python_version < \"3.8\""} [package.extras] -format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"] -format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"] +format-nongpl = ["webcolors (>=1.11)", "uri-template", "rfc3986-validator (>0.1.0)", "rfc3339-validator", "jsonpointer (>1.13)", "isoduration", "idna", "fqdn"] +format = ["webcolors (>=1.11)", "uri-template", "rfc3987", "rfc3339-validator", "jsonpointer (>1.13)", "isoduration", "idna", "fqdn"] [[package]] name = "kiwisolver" @@ -866,6 +980,23 @@ category = "main" optional = false python-versions = ">=3.7" +[[package]] +name = "mako" +version = "1.2.1" +description = "A super-fast templating language that borrows the best ideas from the existing templating languages." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} +MarkupSafe = ">=0.9.2" + +[package.extras] +babel = ["babel"] +lingua = ["lingua"] +testing = ["pytest"] + [[package]] name = "markdown" version = "3.3.6" @@ -878,7 +1009,7 @@ python-versions = ">=3.6" importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""} [package.extras] -testing = ["coverage", "pyyaml"] +testing = ["pyyaml", "coverage"] [[package]] name = "markdown-include" @@ -1020,7 +1151,7 @@ optional = true python-versions = "*" [package.extras] -test = ["codecov (>=2.0.5)", "coverage (>=4.2)", "flake8 (>=3.0.4)", "pytest (>=4.5.0)", "pytest-cov (>=2.7.1)", "pytest-runner (>=5.1)", "pytest-virtualenv (>=1.7.0)", "virtualenv (>=15.0.3)"] +test = ["virtualenv (>=15.0.3)", "pytest-virtualenv (>=1.7.0)", "pytest-runner (>=5.1)", "pytest-cov (>=2.7.1)", "pytest (>=4.5.0)", "flake8 (>=3.0.4)", "coverage (>=4.2)", "codecov (>=2.0.5)"] [[package]] name = "nodeenv" @@ -1110,6 +1241,53 @@ jaxlib = ">=0.1.37" numpy = ">=1.18.0" typing-extensions = ">=3.10.0" +[[package]] +name = "optuna" +version = "2.10.1" +description = "A hyperparameter optimization framework" +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +alembic = "*" +cliff = "*" +cmaes = ">=0.8.2" +colorlog = "*" +numpy = "*" +packaging = ">=20.0" +PyYAML = "*" +scipy = "!=1.4.0" +sqlalchemy = ">=1.1.0" +tqdm = "*" + +[package.extras] +benchmark = ["asv", "virtualenv"] +checking = ["black", "hacking", "isort", "mypy (==0.790)", "blackdoc"] +codecov = ["codecov", "pytest-cov"] +doctest = ["cma", "matplotlib (>=3.0.0)", "pandas", "plotly (>=4.0.0)", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "mlflow"] +document = ["Jinja2 (<3.0.0)", "MarkupSafe (<=2.0.1)", "sphinx (<=3.5.4)", "sphinx-rtd-theme (<=1.0.0)", "sphinx-copybutton (<=0.4.0)", "sphinx-gallery (<=0.10.0)", "sphinx-plotly-directive (<=0.1.3)", "pillow", "matplotlib", "scikit-learn (<1.0.0)", "plotly (>=4.0.0)", "pandas", "lightgbm", "torch (==1.8.0)", "torchvision (==0.9.0)", "torchaudio (==0.8.0)", "thop"] +experimental = ["redis"] +integration = ["chainer (>=5.0.0)", "cma", "lightgbm", "mlflow", "wandb", "mpi4py", "mxnet", "pandas", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "xgboost", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning (>=1.0.2)", "skorch", "catalyst (>=21.3)", "torchaudio (==0.8.0)", "allennlp (>=2.2.0,<2.7.0)", "fastai", "botorch (>=0.4.0)", "torch (==1.8.0+cpu)", "torchvision (==0.9.0+cpu)", "torch (==1.8.0)", "torchvision (==0.9.0)"] +optional = ["bokeh (<2.0.0)", "matplotlib (>=3.0.0)", "pandas", "plotly (>=4.0.0)", "redis", "scikit-learn (>=0.24.2,<1.0.0)"] +testing = ["bokeh (<2.0.0)", "chainer (>=5.0.0)", "cma", "fakeredis", "lightgbm", "matplotlib (>=3.0.0)", "mlflow", "mpi4py", "mxnet", "pandas", "plotly (>=4.0.0)", "pytest", "scikit-learn (>=0.24.2,<1.0.0)", "scikit-optimize", "xgboost", "tensorflow", "tensorflow-datasets", "pytorch-ignite", "pytorch-lightning (>=1.0.2)", "skorch", "catalyst (>=21.3)", "torchaudio (==0.8.0)", "allennlp (>=2.2.0,<2.7.0)", "fastai", "botorch (>=0.4.0)", "torch (==1.8.0+cpu)", "torchvision (==0.9.0+cpu)", "torch (==1.8.0)", "torchvision (==0.9.0)"] +tests = ["fakeredis", "pytest"] + +[[package]] +name = "optuna-dashboard" +version = "0.7.2" +description = "Real-time dashboard for Optuna." +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +bottle = "*" +optuna = ">=2.4" +packaging = "*" +scikit-learn = "*" +typing-extensions = {version = "*", markers = "python_version < \"3.8\""} + [[package]] name = "packaging" version = "21.3" @@ -1131,15 +1309,15 @@ python-versions = ">=3.7.1" [package.dependencies] numpy = [ - {version = ">=1.17.3", markers = "platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\""}, - {version = ">=1.19.2", markers = "platform_machine == \"aarch64\" and python_version < \"3.10\""}, {version = ">=1.20.0", markers = "platform_machine == \"arm64\" and python_version < \"3.10\""}, + {version = ">=1.19.2", markers = "platform_machine == \"aarch64\" and python_version < \"3.10\""}, + {version = ">=1.17.3", markers = "platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\""}, ] python-dateutil = ">=2.7.3" pytz = ">=2017.3" [package.extras] -test = ["hypothesis (>=3.58)", "pytest (>=6.0)", "pytest-xdist"] +test = ["pytest-xdist", "pytest (>=6.0)", "hypothesis (>=3.58)"] [[package]] name = "pathtools" @@ -1149,6 +1327,14 @@ category = "main" optional = false python-versions = "*" +[[package]] +name = "pbr" +version = "5.10.0" +description = "Python Build Reasonableness" +category = "main" +optional = true +python-versions = ">=2.6" + [[package]] name = "pettingzoo" version = "1.18.1" @@ -1208,8 +1394,8 @@ python-versions = ">=3.6" importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""} [package.extras] -dev = ["pre-commit", "tox"] -testing = ["pytest", "pytest-benchmark"] +testing = ["pytest-benchmark", "pytest"] +dev = ["tox", "pre-commit"] [[package]] name = "pre-commit" @@ -1228,6 +1414,21 @@ pyyaml = ">=5.1" toml = "*" virtualenv = ">=20.0.8" +[[package]] +name = "prettytable" +version = "3.3.0" +description = "A simple Python library for easily displaying tabular data in a visually appealing ASCII table format" +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} +wcwidth = "*" + +[package.extras] +tests = ["pytest", "pytest-cov", "pytest-lazy-fixture"] + [[package]] name = "procgen" version = "0.10.7" @@ -1243,7 +1444,7 @@ gym3 = ">=0.3.3,<1.0.0" numpy = ">=1.17.0,<2.0.0" [package.extras] -test = ["pytest (==6.2.5)", "pytest-benchmark (==3.4.1)"] +test = ["pytest-benchmark (==3.4.1)", "pytest (==6.2.5)"] [[package]] name = "promise" @@ -1276,7 +1477,7 @@ optional = false python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [package.extras] -test = ["ipaddress", "mock", "unittest2", "enum34", "pywin32", "wmi"] +test = ["wmi", "pywin32", "enum34", "unittest2", "mock", "ipaddress"] [[package]] name = "py" @@ -1379,7 +1580,23 @@ optional = false python-versions = ">=3.6" [package.extras] -diagrams = ["jinja2", "railroad-diagrams"] +diagrams = ["railroad-diagrams", "jinja2"] + +[[package]] +name = "pyperclip" +version = "1.8.2" +description = "A cross-platform clipboard module for Python. (Only handles plain text for now.)" +category = "main" +optional = true +python-versions = "*" + +[[package]] +name = "pyreadline3" +version = "3.4.1" +description = "A python implementation of GNU readline." +category = "main" +optional = true +python-versions = "*" [[package]] name = "pyrsistent" @@ -1409,7 +1626,7 @@ py = ">=1.8.2" toml = "*" [package.extras] -testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"] +testing = ["xmlschema", "requests", "nose", "mock", "hypothesis (>=3.56)", "argcomplete"] [[package]] name = "python-dateutil" @@ -1507,8 +1724,8 @@ packaging = ">=20.4" typing-extensions = {version = "*", markers = "python_version < \"3.8\""} [package.extras] +ocsp = ["requests (>=2.26.0)", "pyopenssl (==20.0.1)", "cryptography (>=36.0.1)"] hiredis = ["hiredis (>=1.0.0)"] -ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"] [[package]] name = "requests" @@ -1604,6 +1821,26 @@ botocore = ">=1.12.36,<2.0a.0" [package.extras] crt = ["botocore[crt] (>=1.20.29,<2.0a.0)"] +[[package]] +name = "scikit-learn" +version = "1.0.2" +description = "A set of python modules for machine learning and data mining" +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +joblib = ">=0.11" +numpy = ">=1.14.6" +scipy = ">=1.1.0" +threadpoolctl = ">=2.0.0" + +[package.extras] +tests = ["pyamg (>=4.0.0)", "mypy (>=0.770)", "black (>=21.6b0)", "flake8 (>=3.8.2)", "pytest-cov (>=2.9.0)", "pytest (>=5.0.1)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"] +examples = ["seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"] +docs = ["sphinxext-opengraph (>=0.4.2)", "sphinx-prompt (>=1.3.0)", "Pillow (>=7.1.2)", "numpydoc (>=1.0.0)", "sphinx-gallery (>=0.7.0)", "sphinx (>=4.0.1)", "memory-profiler (>=0.57.0)", "seaborn (>=0.9.0)", "pandas (>=0.25.0)", "scikit-image (>=0.14.5)", "matplotlib (>=2.2.3)"] +benchmark = ["memory-profiler (>=0.57.0)", "pandas (>=0.25.0)", "matplotlib (>=2.2.3)"] + [[package]] name = "scipy" version = "1.7.3" @@ -1710,6 +1947,39 @@ category = "main" optional = false python-versions = ">=3.6" +[[package]] +name = "sqlalchemy" +version = "1.4.40" +description = "Database Abstraction Library" +category = "main" +optional = true +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" + +[package.dependencies] +greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"} +importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} + +[package.extras] +aiomysql = ["greenlet (!=0.4.17)", "aiomysql"] +aiosqlite = ["typing_extensions (!=3.10.0.1)", "greenlet (!=0.4.17)", "aiosqlite"] +asyncio = ["greenlet (!=0.4.17)"] +asyncmy = ["greenlet (!=0.4.17)", "asyncmy (>=0.2.3,!=0.2.4)"] +mariadb_connector = ["mariadb (>=1.0.1,!=1.1.2)"] +mssql = ["pyodbc"] +mssql_pymssql = ["pymssql"] +mssql_pyodbc = ["pyodbc"] +mypy = ["sqlalchemy2-stubs", "mypy (>=0.910)"] +mysql = ["mysqlclient (>=1.4.0,<2)", "mysqlclient (>=1.4.0)"] +mysql_connector = ["mysql-connector-python"] +oracle = ["cx_oracle (>=7,<8)", "cx_oracle (>=7)"] +postgresql = ["psycopg2 (>=2.7)"] +postgresql_asyncpg = ["greenlet (!=0.4.17)", "asyncpg"] +postgresql_pg8000 = ["pg8000 (>=1.16.6,!=1.29.0)"] +postgresql_psycopg2binary = ["psycopg2-binary"] +postgresql_psycopg2cffi = ["psycopg2cffi"] +pymysql = ["pymysql (<1)", "pymysql"] +sqlcipher = ["sqlcipher3-binary"] + [[package]] name = "stable-baselines3" version = "1.2.0" @@ -1727,9 +1997,21 @@ pandas = "*" torch = ">=1.8.1" [package.extras] -docs = ["sphinx", "sphinx-autobuild", "sphinx-rtd-theme", "sphinxcontrib.spelling", "sphinx-autodoc-typehints"] -extra = ["opencv-python", "atari-py (>=0.2.0,<0.3.0)", "pillow", "tensorboard (>=2.2.0)", "psutil"] -tests = ["pytest", "pytest-cov", "pytest-env", "pytest-xdist", "pytype", "flake8 (>=3.8)", "flake8-bugbear", "isort (>=5.0)", "black", "scipy (>=1.4.1)"] +tests = ["scipy (>=1.4.1)", "black", "isort (>=5.0)", "flake8-bugbear", "flake8 (>=3.8)", "pytype", "pytest-xdist", "pytest-env", "pytest-cov", "pytest"] +extra = ["psutil", "tensorboard (>=2.2.0)", "pillow", "atari-py (>=0.2.0,<0.3.0)", "opencv-python"] +docs = ["sphinx-autodoc-typehints", "sphinxcontrib.spelling", "sphinx-rtd-theme", "sphinx-autobuild", "sphinx"] + +[[package]] +name = "stevedore" +version = "3.5.0" +description = "Manage dynamic plugins for Python applications" +category = "main" +optional = true +python-versions = ">=3.6" + +[package.dependencies] +importlib-metadata = {version = ">=1.7.0", markers = "python_version < \"3.8\""} +pbr = ">=2.0.0,<2.1.0 || >2.1.0" [[package]] name = "supersuit" @@ -1801,6 +2083,14 @@ category = "main" optional = false python-versions = "*" +[[package]] +name = "threadpoolctl" +version = "3.1.0" +description = "threadpoolctl" +category = "main" +optional = true +python-versions = ">=3.6" + [[package]] name = "tinyscaler" version = "1.2.4" @@ -1877,9 +2167,9 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7" colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] -dev = ["py-make (>=0.1.0)", "twine", "wheel"] -notebook = ["ipywidgets (>=6)"] telegram = ["requests"] +notebook = ["ipywidgets (>=6)"] +dev = ["wheel", "twine", "py-make (>=0.1.0)"] [[package]] name = "types-protobuf" @@ -1906,9 +2196,9 @@ optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4" [package.extras] -brotli = ["brotlipy (>=0.6.0)"] -secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "ipaddress"] socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] +secure = ["ipaddress", "certifi", "idna (>=2.0.0)", "cryptography (>=1.3.4)", "pyOpenSSL (>=0.14)"] +brotli = ["brotlipy (>=0.6.0)"] [[package]] name = "virtualenv" @@ -1955,14 +2245,14 @@ six = ">=1.13.0" yaspin = ">=1.0.0" [package.extras] -aws = ["boto3"] -azure = ["azure-storage-blob"] -gcp = ["google-cloud-storage"] +sweeps = ["pydantic (>=1.8.2)", "jsonref (>=0.2)", "jsonschema (>=3.2.0)", "scikit-learn (==0.24.1)", "pyyaml", "scipy (>=1.5.4)", "numpy (>=1.15,<1.21)"] +media = ["rdkit-pypi", "plotly", "soundfile", "bokeh", "pillow", "moviepy", "numpy"] +launch = ["yaspin", "typing-extensions", "iso8601", "chardet", "nbconvert", "jupyter-repo2docker"] +kubeflow = ["sh", "google-cloud-storage", "minio", "kubernetes"] grpc = ["grpcio (>=1.27.2)"] -kubeflow = ["kubernetes", "minio", "google-cloud-storage", "sh"] -launch = ["jupyter-repo2docker", "nbconvert", "chardet", "iso8601", "typing-extensions", "yaspin"] -media = ["numpy", "moviepy", "pillow", "bokeh", "soundfile", "plotly", "rdkit-pypi"] -sweeps = ["numpy (>=1.15,<1.21)", "scipy (>=1.5.4)", "pyyaml", "scikit-learn (==0.24.1)", "jsonschema (>=3.2.0)", "jsonref (>=0.2)", "pydantic (>=1.8.2)"] +gcp = ["google-cloud-storage"] +azure = ["azure-storage-blob"] +aws = ["boto3"] [[package]] name = "watchdog" @@ -1975,6 +2265,14 @@ python-versions = ">=3.6" [package.extras] watchmedo = ["PyYAML (>=3.10)"] +[[package]] +name = "wcwidth" +version = "0.2.5" +description = "Measures the displayed width of unicode strings in a terminal" +category = "main" +optional = true +python-versions = "*" + [[package]] name = "werkzeug" version = "2.0.3" @@ -2014,8 +2312,8 @@ optional = false python-versions = ">=3.7" [package.extras] -docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy"] +testing = ["pytest-mypy", "pytest-black (>=0.3.7)", "func-timeout", "jaraco.itertools", "pytest-enabler (>=1.0.1)", "pytest-cov", "pytest-flake8", "pytest-checkdocs (>=2.4)", "pytest (>=6)"] +docs = ["rst.linker (>=1.9)", "jaraco.packaging (>=8.2)", "sphinx"] [extras] atari = ["ale-py", "AutoROM"] @@ -2025,6 +2323,7 @@ envpool = ["envpool"] isaacgym = ["isaacgym", "isaacgymenvs"] jax = ["jax", "jaxlib", "flax"] mujoco = ["free-mujoco-py"] +optuna = ["optuna", "optuna-dashboard", "rich"] pettingzoo = ["pettingzoo", "pygame", "pymunk", "SuperSuit", "multi-agent-ale-py"] plot = ["pandas", "seaborn"] procgen = ["procgen"] @@ -2034,7 +2333,7 @@ pytest = ["pytest"] [metadata] lock-version = "1.1" python-versions = ">=3.7.1,<3.10" -content-hash = "5da6be48ac485427f7dcd8e4592808c9e7fe15be5110d3903de474101181b77c" +content-hash = "01cea0d96c7474e4b1d0edbb015bc8b07afaf5df8812b61603838983bdf820e4" [metadata.files] absl-py = [ @@ -2058,7 +2357,13 @@ ale-py = [ {file = "ale_py-0.7.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3f65371c180779b115d8600d99780e9e83b229812e94c6b49be1686ce4d82573"}, {file = "ale_py-0.7.4-cp39-cp39-win_amd64.whl", hash = "sha256:b53e7d0c8f8e8610ebaec88887da2427ce16811f9697ccbb39588ec784bea145"}, ] -antlr4-python3-runtime = [] +alembic = [ + {file = "alembic-1.8.1-py3-none-any.whl", hash = "sha256:0a024d7f2de88d738d7395ff866997314c837be6104e90c5724350313dee4da4"}, + {file = "alembic-1.8.1.tar.gz", hash = "sha256:cd0b5e45b14b706426b833f06369b9a6d5ee03f826ec3238723ce8caaf6e5ffa"}, +] +antlr4-python3-runtime = [ + {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, +] async-timeout = [ {file = "async-timeout-4.0.2.tar.gz", hash = "sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15"}, {file = "async_timeout-4.0.2-py3-none-any.whl", hash = "sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"}, @@ -2071,6 +2376,10 @@ attrs = [ {file = 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"sha256:b47250dd24f92b7dd6a0a8fc5244da14608f3ca90a5efcd37a3b1642fac9a375"}, {file = "zipp-3.7.0.tar.gz", hash = "sha256:9f50f446828eb9d45b267433fd3e9da8d801f614129124863f9c51ebceafb87d"}, -] \ No newline at end of file +] diff --git a/pyproject.toml b/pyproject.toml index ef282f506..941d6dcf8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,6 +43,9 @@ isaacgym = {path = "cleanrl/ppo_continuous_action_isaacgym/isaacgym", optional = isaacgymenvs = {git = "https://github.com/vwxyzjn/IsaacGymEnvs.git", rev = "poetry", optional = true} markdown-include = {version = "^0.7.0", optional = true} numpy = "~1.21" +optuna = {version = "^2.10.1", optional = true} +optuna-dashboard = {version = "^0.7.2", optional = true} +rich = {version = "<12.0", optional = true} [tool.poetry.dev-dependencies] pre-commit = "^2.17.0" @@ -64,3 +67,4 @@ docs = ["mkdocs-material", "markdown-include"] envpool = ["envpool"] jax = ["jax", "jaxlib", "flax"] isaacgym = ["isaacgym", "isaacgymenvs"] +optuna = ["optuna", "optuna-dashboard", "rich"] From 0fda2828a544289e9aac7ce9cd052fc5c096cfb2 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Tue, 23 Aug 2022 09:25:30 -0400 Subject: [PATCH 13/20] Quick fix --- cleanrl_utils/tuner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cleanrl_utils/tuner.py b/cleanrl_utils/tuner.py index 2f7e94920..2d2f5b818 100644 --- a/cleanrl_utils/tuner.py +++ b/cleanrl_utils/tuner.py @@ -41,7 +41,7 @@ def __init__( self.metric = metric self.target_scores = target_scores if len(self.target_scores) > 1: - if self.target_scores.values()[0] is None: + if None in self.target_scores.values(): raise ValueError( "If there are multiple environments, the target scores must be specified for each environment." ) From 773ad721278e12374e4779af156f4dbd57c71610 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Wed, 24 Aug 2022 17:23:11 -0400 Subject: [PATCH 14/20] clarification --- docs/advanced/hyperparameter-tuning.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/advanced/hyperparameter-tuning.md b/docs/advanced/hyperparameter-tuning.md index 10ee83096..0a03afb4a 100644 --- a/docs/advanced/hyperparameter-tuning.md +++ b/docs/advanced/hyperparameter-tuning.md @@ -105,7 +105,7 @@ Note that we are using 3 random seeds for each environment in `["CartPole-v1","A ???+ info - When optimizing Atari games, you can put the human normalized scores in `target_scores` (Mnih et al., 2015, Extended Data Table 2)[^1], as done in the following example. The first number for each environment is the score obtained by random play and the second number is the score obtained by professional game testers. + When optimizing Atari games, you can put the human normalized scores in `target_scores` (Mnih et al., 2015, Extended Data Table 2)[^1], as done in the following example. The first number for each environment is the score obtained by random play and the second number is the score obtained by professional game testers. Note here we using `aggregation_type="median"`, this means we will optimize for the *median* of the human normalized scores *averaged* over the `num_seeds=3` *experiments* for each trial. ```python tuner = Tuner( @@ -113,7 +113,7 @@ Note that we are using 3 random seeds for each environment in `["CartPole-v1","A metric="charts/episodic_return", metric_last_n_average_window=50, direction="maximize", - aggregation_type="average", + aggregation_type="median", target_scores={ "Alien-v5": [227.8, 6875], "Amidar-v5": [5.8, 1676], @@ -122,6 +122,7 @@ Note that we are using 3 random seeds for each environment in `["CartPole-v1","A 'Asteroids-v5': (719.1, 13157), ... }, + num_seeds=3, ... ) ``` From ff25c608097d41317615c346277035d98ea9501b Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Thu, 25 Aug 2022 11:00:46 -0400 Subject: [PATCH 15/20] Update docs on python version --- README.md | 2 +- docs/get-started/installation.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ceeb2f311..dafb321ca 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ Good luck have fun :rocket: ## Get started Prerequisites: -* Python 3.8-3.9 (not yet 3.10) +* >=3.7.1,<3.10 (not yet 3.10) * [Poetry](https://python-poetry.org) To run experiments locally, give the following a try: diff --git a/docs/get-started/installation.md b/docs/get-started/installation.md index f2c1f6797..fe58bb32f 100644 --- a/docs/get-started/installation.md +++ b/docs/get-started/installation.md @@ -2,7 +2,7 @@ ## Prerequisites -* Python 3.8-3.9 (not yet 3.10) +* >=3.7.1,<3.10 (not yet 3.10) * [Poetry](https://python-poetry.org) Simply run the following command for a quick start From d6debc2c79ccb7db92574341502fe28be5aba518 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Thu, 25 Aug 2022 11:02:27 -0400 Subject: [PATCH 16/20] add test cases --- tests/test_tuner.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 tests/test_tuner.py diff --git a/tests/test_tuner.py b/tests/test_tuner.py new file mode 100644 index 000000000..af2f1ef8a --- /dev/null +++ b/tests/test_tuner.py @@ -0,0 +1,32 @@ +import optuna + +from cleanrl_utils.tuner import Tuner + +def test_tuner(): + tuner = Tuner( + script="cleanrl/ppo.py", + metric="charts/episodic_return", + metric_last_n_average_window=50, + direction="maximize", + target_scores={ + "CartPole-v1": [0, 500], + "Acrobot-v1": [-500, 0], + }, + params_fn=lambda trial: { + "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), + "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "num-steps": trial.suggest_categorical("num-steps", [5]), + "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), + "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), + "total-timesteps": 100, + "num-envs": 4, + }, + pruner=optuna.pruners.MedianPruner(n_startup_trials=5), + sampler=optuna.samplers.TPESampler(), + # wandb_kwargs={"project": "cleanrl"}, + ) + tuner.tune( + num_trials=1, + num_seeds=1, + ) From fc45151dce010ad91da1d007a773db6765a08211 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Thu, 25 Aug 2022 11:03:54 -0400 Subject: [PATCH 17/20] add tests --- .github/workflows/tests.yaml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index 103ad104a..e10774293 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -42,6 +42,10 @@ jobs: - name: Run core tests with jax if: runner.os == 'Linux' || runner.os == 'macOS' run: poetry run pytest tests/test_classic_control_jax.py + - name: Install tuner dependencies + run: poetry install -E optuna + - name: Run tuner tests + run: poetry run pytest tests/test_tuner.py test-atari-envs: strategy: From 8d43bda4d8654114b64f935d657e7009fa6e31d1 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Thu, 25 Aug 2022 11:15:48 -0400 Subject: [PATCH 18/20] update config --- tests/test_tuner.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_tuner.py b/tests/test_tuner.py index af2f1ef8a..2f7f8f9d5 100644 --- a/tests/test_tuner.py +++ b/tests/test_tuner.py @@ -2,6 +2,7 @@ from cleanrl_utils.tuner import Tuner + def test_tuner(): tuner = Tuner( script="cleanrl/ppo.py", From 14b7bf538ae06b4a3c14cf860e4f990376ebd2f4 Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Thu, 25 Aug 2022 17:03:43 -0400 Subject: [PATCH 19/20] update test cases --- tests/test_tuner.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_tuner.py b/tests/test_tuner.py index 2f7f8f9d5..c4c0d1d32 100644 --- a/tests/test_tuner.py +++ b/tests/test_tuner.py @@ -17,11 +17,11 @@ def test_tuner(): "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), - "num-steps": trial.suggest_categorical("num-steps", [5]), + "num-steps": trial.suggest_categorical("num-steps", [1200]), "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), - "total-timesteps": 100, - "num-envs": 4, + "total-timesteps": 1200, + "num-envs": 1, }, pruner=optuna.pruners.MedianPruner(n_startup_trials=5), sampler=optuna.samplers.TPESampler(), From bbe27fae6082f3c48db0706219ae122d44908aed Mon Sep 17 00:00:00 2001 From: Costa Huang Date: Thu, 25 Aug 2022 21:53:40 -0400 Subject: [PATCH 20/20] Refactor docs --- docs/advanced/hyperparameter-tuning.md | 158 +++++++++++++++---------- docs/advanced/optuna-dashboard-1.png | Bin 0 -> 564381 bytes docs/advanced/optuna-dashboard-2.png | Bin 0 -> 727194 bytes docs/advanced/optuna-dashboard.png | Bin 901780 -> 0 bytes docs/advanced/optuna-results.png | Bin 0 -> 293984 bytes 5 files changed, 98 insertions(+), 60 deletions(-) create mode 100644 docs/advanced/optuna-dashboard-1.png create mode 100644 docs/advanced/optuna-dashboard-2.png delete mode 100644 docs/advanced/optuna-dashboard.png create mode 100644 docs/advanced/optuna-results.png diff --git a/docs/advanced/hyperparameter-tuning.md b/docs/advanced/hyperparameter-tuning.md index 0a03afb4a..65eb7ffc5 100644 --- a/docs/advanced/hyperparameter-tuning.md +++ b/docs/advanced/hyperparameter-tuning.md @@ -1,13 +1,17 @@ # Hyperparameter Tuning -CleanRL comes with a simple and practical hyperparameter tuning utility `Tuner`. +CleanRL comes with a lightweight hyperparameter tuning utility `Tuner`, with a primary purpose of helping researchers find **a single set of hyperparameters that works well with multiple tasks of similar type** (e.g., one set of parameters for MuJoCo, another set for Atari). We try to avoid tuning hyperparameters per task, which is usually adhoc and do not generalize. ## Get started -Create the following file: +Consider the [:material-github: `tuner_example.py`](https://github.com/vwxyzjn/cleanrl/blob/master/tuner_example.py) in the CleanRL's root directory, which has the following content: + ```python title="tuner_example.py" +import optuna + from cleanrl_utils.tuner import Tuner + tuner = Tuner( script="cleanrl/ppo.py", metric="charts/episodic_return", @@ -15,18 +19,21 @@ tuner = Tuner( direction="maximize", aggregation_type="average", target_scores={ - "CartPole-v1": None, + "CartPole-v1": [0, 500], + "Acrobot-v1": [-500, 0], }, params_fn=lambda trial: { "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), - "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4, 8]), "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), - "total-timesteps": 10000, - "num-envs": 4, + "total-timesteps": 100000, + "num-envs": 16, }, + pruner=optuna.pruners.MedianPruner(n_startup_trials=5), + sampler=optuna.samplers.TPESampler(), ) tuner.tune( num_trials=100, @@ -34,42 +41,98 @@ tuner.tune( ) ``` -Then you can run the tuner with +You can run the tuner with the following command: ```bash poetry install -E optuna python tuner_example.py ``` -Here is what happened: + -1. The `tuner_example.py` launches `num_trials=100` *trials* to find the best single set of hyperparameters for `CartPole-v1` in `script="cleanrl/ppo.py"`. + +Often it is useful to find a single set of hyper parameters that work well with multiple environments, but this is challenging **because each environment may have different reward scales**, so we need to normalize the reward scales. + +This is where `target_scores` comes in. We can use it to specify an upper and lower threshold of rewards for each environment (they don't have to be exact boundaries and can be just ballpark estimates). For example, if we want to find a set of hyper parameters that work well with `CartPole-v1` and `Acrobot-v1`, we can set the `target_scores` and `aggregation_type` as follows: + +```python +tuner = Tuner( + ..., + target_scores={ + "CartPole-v1": [0, 500], + "Acrobot-v1": [-500, 0], + } + aggregation_type="average", +) +``` + + +Here is what happened when running `python tuner_example.py`: + +1. The `tuner_example.py` launches `num_trials=100` *trials* to find the best single set of hyperparameters for `CartPole-v1` and `Acrobot-v1` in `script="cleanrl/ppo.py"`. 1. Each *trial* samples a set of hyperparameters from the `params_fn` to run `num_seeds=3` *experiments* with different random seeds, mitigating the impact of randomness on the results. - * In each *experiment*, `tuner_example.py` averages the last `metric_last_n_average_window=50` reported `metric="charts/episodic_return"` to a number $x_i$ and calculate a normalized score $z_i$ according to the `target_scores`. We will talk more about `target_scores` which is useful when working with multiple environments in the next section, but in this section we just set `None` as the target score for `CartPole-v1` — this means we will set the normalized score $z_i = x_i$. + * In each *experiment*, `tuner_example.py` averages the last `metric_last_n_average_window=50` reported `metric="charts/episodic_return"` to a number $x_i$ and calculate a normalized score $z_i$ according to the `target_scores`. In this case, $z_{i_0} = (x_{i_0} - 0) / (500 - 0)$ and $z_{i_1} = (x_{i_1} - -500) / (0 - -500)$. Then the tuner will `aggregation_type="average"` the two scores to get the normalized score $z_i = (z_{i_0} + z_{i_1}) / 2$. 1. Each *trial* then averages the normalized scores $z_i$ of the `num_seeds=3` *experiments* to a number $z$ and the tuner optimizes $z$ according `direction="maximize"`. +Note that we are using 3 random seeds for each environment in `["CartPole-v1","Acrobot-v1"]`, totalling `2*3=6` experiments per trial. + + +???+ info + + When optimizing Atari games, you can put the human normalized scores in `target_scores` (Mnih et al., 2015, Extended Data Table 2)[^1], as done in the following example. The first number for each environment is the score obtained by random play and the second number is the score obtained by professional game testers. Note here we using `aggregation_type="median"`, this means we will optimize for the **median** of the human normalized scores **averaged** over the `num_seeds=3` experiments for each trial (pay close attention to the phrasing). + + ```python + tuner = Tuner( + script="cleanrl/ppo_atari_envpool.py", + metric="charts/episodic_return", + metric_last_n_average_window=50, + direction="maximize", + aggregation_type="median", + target_scores={ + "Alien-v5": [227.8, 6875], + "Amidar-v5": [5.8, 1676], + 'Assault-v5': (222.4, 1496), + 'Asterix-v5': (210.0, 8503), + 'Asteroids-v5': (719.1, 13157), + ... + }, + num_seeds=3, + ... + ) + ``` + ## Visualization -Running `python tuner_example.py` will create a sqlite database containing all of the hyperparameter trials in `./cleanrl_hpopt.db`. We can use [optuna-dashboard](https://github.com/optuna/optuna-dashboard) to visualize the process. +Running `python tuner_example.py` will create a sqlite database containing all of the hyperparameter trials in `./cleanrl_hpopt.db` We can use [optuna-dashboard](https://github.com/optuna/optuna-dashboard) to visualize the process. ```bash poetry run optuna-dashboard sqlite:///cleanrl_hpopt.db ``` -You can use a different database by passing `Tuner(..., storage="mysql://root@localhost/example")` to use a more persistent storage. -![](./optuna-dashboard.png) +![](./optuna-dashboard-1.png) +In the panel above, the y-axis shows the normalized score of the two environments (`CartPole-v1` and `Acrobot-v1`) and the x-axis shows the number of trials. -## Work w/ multiple environments +![](./optuna-dashboard-2.png) -Often it is useful to find a single set of hyper parameters that work well with multiple environments, but this is challenging **because each environment may have different reward scales**, so we need to do normalization. +The panel above shows the parallel coordinates plot of the normalized score — for example, we can see having a lower learning rate and a higher number of minibatches is a good choice. -This is where `target_scores` comes in. We can use it to specify an upper and lower threshold of rewards for each environment (they don't have to be exact boundaries and can be just ballpark estimates). For example, if we want to find a set of hyper parameters that work well with `CartPole-v1` and `Acrobot-v1`, we can set the `target_scores` and `aggregation_type` as follows: +???+ info + + You can use a different database by passing `Tuner(..., storage="mysql://root@localhost/example")` to use a more persistent storage. + +## Work w/o knowing the reward scales + +What if we don't know the reward scales for the environments? We can optionally set the value `target_scores` to `None`, but this will only work with one environment at a time because it's difficult to optimize without knowing the scale of the rewards in each environment. + + +```python title="tuner_example.py" hl_lines="11-13" +import optuna -```python title="tuner_example_multi_env.py" hl_lines="7-11" from cleanrl_utils.tuner import Tuner + tuner = Tuner( script="cleanrl/ppo.py", metric="charts/episodic_return", @@ -77,19 +140,20 @@ tuner = Tuner( direction="maximize", aggregation_type="average", target_scores={ - "CartPole-v1": [0, 500], - "Acrobot-v1": [-500, 0], + "CartPole-v1": None, }, params_fn=lambda trial: { "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), - "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4, 8]), "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), - "total-timesteps": 10000, + "total-timesteps": 100000, "num-envs": 16, }, + pruner=optuna.pruners.MedianPruner(n_startup_trials=5), + sampler=optuna.samplers.TPESampler(), ) tuner.tune( num_trials=100, @@ -97,44 +161,18 @@ tuner.tune( ) ``` -Then each experiment will calculate the normalized scores $z_{i_0} = (x_{i_0} - 0) / (500 - 0), z_{i_1} = (x_{i_1} - -500) / (0 - -500)$ and the tuner will `aggregation_type="average"` the two scores to get the normalized score $z_i = (z_{i_0} + z_{i_1}) / 2$. - -Note that we are using 3 random seeds for each environment in `["CartPole-v1","Acrobot-v1"]`, totalling `2*3=6` experiments per trial. - - - -???+ info - - When optimizing Atari games, you can put the human normalized scores in `target_scores` (Mnih et al., 2015, Extended Data Table 2)[^1], as done in the following example. The first number for each environment is the score obtained by random play and the second number is the score obtained by professional game testers. Note here we using `aggregation_type="median"`, this means we will optimize for the *median* of the human normalized scores *averaged* over the `num_seeds=3` *experiments* for each trial. - - ```python - tuner = Tuner( - script="cleanrl/ppo_atari.py", - metric="charts/episodic_return", - metric_last_n_average_window=50, - direction="maximize", - aggregation_type="median", - target_scores={ - "Alien-v5": [227.8, 6875], - "Amidar-v5": [5.8, 1676], - 'Assault-v5': (222.4, 1496), - 'Asterix-v5': (210.0, 8503), - 'Asteroids-v5': (719.1, 13157), - ... - }, - num_seeds=3, - ... - ) - ``` +In the example above, we will set the normalized score $z_i$ to be the average of the last `metric_last_n_average_window=50` reported `metric="charts/episodic_return"` for `CartPole-v1`. ## Work w/ pruners and samplers You can use `Tuner` with any [pruner](https://optuna.readthedocs.io/en/stable/reference/pruners.html) from `optuna` to prune less promising experiments or [samplers](https://optuna.readthedocs.io/en/stable/reference/samplers.html) to sample new hyperparameters. If you don't specify them explicitly, the script will use the [default ones](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.create_study.html). -```python title="tuner_example_pruner.py" hl_lines="1 23 24" +```python title="tuner_example.py" hl_lines="1 24 25" import optuna + from cleanrl_utils.tuner import Tuner + tuner = Tuner( script="cleanrl/ppo.py", metric="charts/episodic_return", @@ -142,17 +180,16 @@ tuner = Tuner( direction="maximize", aggregation_type="average", target_scores={ - "CartPole-v1": [0, 500], - "Acrobot-v1": [-500, 0], + "CartPole-v1": None, }, params_fn=lambda trial: { "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), - "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4, 8]), "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), - "total-timesteps": 10000, + "total-timesteps": 100000, "num-envs": 16, }, pruner=optuna.pruners.MedianPruner(n_startup_trials=5), @@ -170,9 +207,11 @@ tuner.tune( The `Tuner` can track all the experiments into [Weights and Biases](https://wandb.ai) to help you visualize the progress of the tuning. -```python title="tuner_example.py" hl_lines="25" +```python title="tuner_example.py" hl_lines="26" import optuna + from cleanrl_utils.tuner import Tuner + tuner = Tuner( script="cleanrl/ppo.py", metric="charts/episodic_return", @@ -180,17 +219,16 @@ tuner = Tuner( direction="maximize", aggregation_type="average", target_scores={ - "CartPole-v1": [0, 500], - "Acrobot-v1": [-500, 0], + "CartPole-v1": None, }, params_fn=lambda trial: { "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), - "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]), + "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4, 8]), "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), - "total-timesteps": 10000, + "total-timesteps": 100000, "num-envs": 16, }, pruner=optuna.pruners.MedianPruner(n_startup_trials=5), diff --git a/docs/advanced/optuna-dashboard-1.png b/docs/advanced/optuna-dashboard-1.png new file mode 100644 index 0000000000000000000000000000000000000000..b7d781c950dd552912c3aa78b4c56c5f73ab55de GIT binary patch literal 564381 zcmeFYXIv9&*FK7(qN2tMVj+~Rh$4|L5QvJ31q49_HbO+CiGcK;5fM>9iXcUVpfnKy z=^`a4Ra)p(h|)q2B#@q&Gts@D_c{Ob^?W$L-+5Ui)9x~}=Dyc`t!u43H@v31ed~d( z0s;ctFJC%uBp|SLy@0?*x6Pa2Gv5Wh8{rd~4mvu9mvwX`4BcF89Uj;S2wZypDqhIY zcvj>Q4l%q2xD6@j99HwTYs zl(d3uQjq_qWdWi0X9P{%J}YiHas2o;3B?>p9{EK;Im=}zvVqMYLAmc~OhtJC!@b{f 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