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on_policy_runner.py
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on_policy_runner.py
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# Copyright 2021 ETH Zurich, NVIDIA CORPORATION
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import os
import statistics
import time
import torch
from collections import deque
from torch.utils.tensorboard import SummaryWriter as TensorboardSummaryWriter
import rsl_rl
from rsl_rl.algorithms import PPO
from rsl_rl.env import VecEnv
from rsl_rl.modules import ActorCritic, ActorCriticRecurrent, EmpiricalNormalization
from rsl_rl.utils import store_code_state
class OnPolicyRunner:
"""On-policy runner for training and evaluation."""
def __init__(self, env: VecEnv, train_cfg, log_dir=None, device="cpu"):
self.cfg = train_cfg
self.alg_cfg = train_cfg["algorithm"]
self.policy_cfg = train_cfg["policy"]
self.device = device
self.env = env
obs, extras = self.env.get_observations()
num_obs = obs.shape[1]
if "critic" in extras["observations"]:
num_critic_obs = extras["observations"]["critic"].shape[1]
else:
num_critic_obs = num_obs
actor_critic_class = eval(self.policy_cfg.pop("class_name")) # ActorCritic
actor_critic: ActorCritic | ActorCriticRecurrent = actor_critic_class(
num_obs, num_critic_obs, self.env.num_actions, **self.policy_cfg
).to(self.device)
alg_class = eval(self.alg_cfg.pop("class_name")) # PPO
self.alg: PPO = alg_class(actor_critic, device=self.device, **self.alg_cfg)
self.num_steps_per_env = self.cfg["num_steps_per_env"]
self.save_interval = self.cfg["save_interval"]
self.empirical_normalization = self.cfg["empirical_normalization"]
if self.empirical_normalization:
if train_cfg.get("resume") == True:
until = 0
else:
until = 1.0e8
self.obs_normalizer = EmpiricalNormalization(shape=[num_obs], until=until).to(self.device)
self.critic_obs_normalizer = EmpiricalNormalization(shape=[num_critic_obs], until=until).to(self.device)
else:
self.obs_normalizer = torch.nn.Identity() # no normalization
self.critic_obs_normalizer = torch.nn.Identity() # no normalization
# init storage and model
self.alg.init_storage(
self.env.num_envs,
self.num_steps_per_env,
[num_obs],
[num_critic_obs],
[self.env.num_actions],
)
# Log
self.log_dir = log_dir
self.writer = None
self.tot_timesteps = 0
self.tot_time = 0
self.current_learning_iteration = 0
self.git_status_repos = [rsl_rl.__file__]
def learn(self, num_learning_iterations: int, init_at_random_ep_len: bool = False):
# initialize writer
if self.log_dir is not None and self.writer is None:
# Launch either Tensorboard or Neptune & Tensorboard summary writer(s), default: Tensorboard.
self.logger_type = self.cfg.get("logger", "tensorboard")
self.logger_type = self.logger_type.lower()
if self.logger_type == "neptune":
from rsl_rl.utils.neptune_utils import NeptuneSummaryWriter
self.writer = NeptuneSummaryWriter(log_dir=self.log_dir, flush_secs=10, cfg=self.cfg)
self.writer.log_config(self.env.cfg, self.cfg, self.alg_cfg, self.policy_cfg)
elif self.logger_type == "wandb":
from rsl_rl.utils.wandb_utils import WandbSummaryWriter
self.writer = WandbSummaryWriter(log_dir=self.log_dir, flush_secs=10, cfg=self.cfg)
self.writer.log_config(self.env.cfg, self.cfg, self.alg_cfg, self.policy_cfg)
elif self.logger_type == "tensorboard":
self.writer = TensorboardSummaryWriter(log_dir=self.log_dir, flush_secs=10)
else:
raise AssertionError("logger type not found")
if init_at_random_ep_len:
self.env.episode_length_buf = torch.randint_like(
self.env.episode_length_buf, high=int(self.env.max_episode_length)
)
obs, extras = self.env.get_observations()
critic_obs = extras["observations"].get("critic", obs)
obs, critic_obs = obs.to(self.device), critic_obs.to(self.device)
self.train_mode() # switch to train mode (for dropout for example)
ep_infos = []
rewbuffer = deque(maxlen=100)
lenbuffer = deque(maxlen=100)
cur_reward_sum = torch.zeros(self.env.num_envs, dtype=torch.float, device=self.device)
cur_episode_length = torch.zeros(self.env.num_envs, dtype=torch.float, device=self.device)
start_iter = self.current_learning_iteration
tot_iter = start_iter + num_learning_iterations
for it in range(start_iter, tot_iter):
start = time.time()
# Rollout
with torch.inference_mode():
for i in range(self.num_steps_per_env):
actions = self.alg.act(obs, critic_obs)
obs, rewards, dones, infos = self.env.step(actions)
obs = self.obs_normalizer(obs)
if "critic" in infos["observations"]:
critic_obs = self.critic_obs_normalizer(infos["observations"]["critic"])
else:
critic_obs = obs
obs, critic_obs, rewards, dones = (
obs.to(self.device),
critic_obs.to(self.device),
rewards.to(self.device),
dones.to(self.device),
)
self.alg.process_env_step(rewards, dones, infos)
if self.log_dir is not None:
# Book keeping
# note: we changed logging to use "log" instead of "episode" to avoid confusion with
# different types of logging data (rewards, curriculum, etc.)
if "episode" in infos:
ep_infos.append(infos["episode"])
elif "log" in infos:
ep_infos.append(infos["log"])
cur_reward_sum += rewards
cur_episode_length += 1
new_ids = (dones > 0).nonzero(as_tuple=False)
rewbuffer.extend(cur_reward_sum[new_ids][:, 0].cpu().numpy().tolist())
lenbuffer.extend(cur_episode_length[new_ids][:, 0].cpu().numpy().tolist())
cur_reward_sum[new_ids] = 0
cur_episode_length[new_ids] = 0
stop = time.time()
collection_time = stop - start
# Learning step
start = stop
self.alg.compute_returns(critic_obs)
mean_value_loss, mean_surrogate_loss = self.alg.update()
stop = time.time()
learn_time = stop - start
self.current_learning_iteration = it
if self.log_dir is not None:
self.log(locals())
if it % self.save_interval == 0:
self.save(os.path.join(self.log_dir, f"model_{it}.pt"))
ep_infos.clear()
if it == start_iter:
# obtain all the diff files
git_file_paths = store_code_state(self.log_dir, self.git_status_repos)
# if possible store them to wandb
if self.logger_type in ["wandb", "neptune"] and git_file_paths:
for path in git_file_paths:
self.writer.save_file(path)
self.save(os.path.join(self.log_dir, f"model_{self.current_learning_iteration}.pt"))
def log(self, locs: dict, width: int = 80, pad: int = 35):
self.tot_timesteps += self.num_steps_per_env * self.env.num_envs
self.tot_time += locs["collection_time"] + locs["learn_time"]
iteration_time = locs["collection_time"] + locs["learn_time"]
ep_string = ""
if locs["ep_infos"]:
for key in locs["ep_infos"][0]:
infotensor = torch.tensor([], device=self.device)
for ep_info in locs["ep_infos"]:
# handle scalar and zero dimensional tensor infos
if key not in ep_info:
continue
if not isinstance(ep_info[key], torch.Tensor):
ep_info[key] = torch.Tensor([ep_info[key]])
if len(ep_info[key].shape) == 0:
ep_info[key] = ep_info[key].unsqueeze(0)
infotensor = torch.cat((infotensor, ep_info[key].to(self.device)))
value = torch.mean(infotensor)
# log to logger and terminal
if "/" in key:
self.writer.add_scalar(key, value, locs["it"])
ep_string += f"""{f'{key}:':>{pad}} {value:.4f}\n"""
else:
self.writer.add_scalar("Episode/" + key, value, locs["it"])
ep_string += f"""{f'Mean episode {key}:':>{pad}} {value:.4f}\n"""
mean_std = self.alg.actor_critic.std.mean()
fps = int(self.num_steps_per_env * self.env.num_envs / (locs["collection_time"] + locs["learn_time"]))
self.writer.add_scalar("Loss/value_function", locs["mean_value_loss"], locs["it"])
self.writer.add_scalar("Loss/surrogate", locs["mean_surrogate_loss"], locs["it"])
self.writer.add_scalar("Loss/learning_rate", self.alg.learning_rate, locs["it"])
self.writer.add_scalar("Policy/mean_noise_std", mean_std.item(), locs["it"])
self.writer.add_scalar("Perf/total_fps", fps, locs["it"])
self.writer.add_scalar("Perf/collection time", locs["collection_time"], locs["it"])
self.writer.add_scalar("Perf/learning_time", locs["learn_time"], locs["it"])
if len(locs["rewbuffer"]) > 0:
self.writer.add_scalar("Train/mean_reward", statistics.mean(locs["rewbuffer"]), locs["it"])
self.writer.add_scalar("Train/mean_episode_length", statistics.mean(locs["lenbuffer"]), locs["it"])
if self.logger_type != "wandb": # wandb does not support non-integer x-axis logging
self.writer.add_scalar("Train/mean_reward/time", statistics.mean(locs["rewbuffer"]), self.tot_time)
self.writer.add_scalar(
"Train/mean_episode_length/time", statistics.mean(locs["lenbuffer"]), self.tot_time
)
str = f" \033[1m Learning iteration {locs['it']}/{locs['tot_iter']} \033[0m "
if len(locs["rewbuffer"]) > 0:
log_string = (
f"""{'#' * width}\n"""
f"""{str.center(width, ' ')}\n\n"""
f"""{'Computation:':>{pad}} {fps:.0f} steps/s (collection: {locs[
'collection_time']:.3f}s, learning {locs['learn_time']:.3f}s)\n"""
f"""{'Value function loss:':>{pad}} {locs['mean_value_loss']:.4f}\n"""
f"""{'Surrogate loss:':>{pad}} {locs['mean_surrogate_loss']:.4f}\n"""
f"""{'Mean action noise std:':>{pad}} {mean_std.item():.2f}\n"""
f"""{'Mean reward:':>{pad}} {statistics.mean(locs['rewbuffer']):.2f}\n"""
f"""{'Mean episode length:':>{pad}} {statistics.mean(locs['lenbuffer']):.2f}\n"""
)
# f"""{'Mean reward/step:':>{pad}} {locs['mean_reward']:.2f}\n"""
# f"""{'Mean episode length/episode:':>{pad}} {locs['mean_trajectory_length']:.2f}\n""")
else:
log_string = (
f"""{'#' * width}\n"""
f"""{str.center(width, ' ')}\n\n"""
f"""{'Computation:':>{pad}} {fps:.0f} steps/s (collection: {locs[
'collection_time']:.3f}s, learning {locs['learn_time']:.3f}s)\n"""
f"""{'Value function loss:':>{pad}} {locs['mean_value_loss']:.4f}\n"""
f"""{'Surrogate loss:':>{pad}} {locs['mean_surrogate_loss']:.4f}\n"""
f"""{'Mean action noise std:':>{pad}} {mean_std.item():.2f}\n"""
)
# f"""{'Mean reward/step:':>{pad}} {locs['mean_reward']:.2f}\n"""
# f"""{'Mean episode length/episode:':>{pad}} {locs['mean_trajectory_length']:.2f}\n""")
log_string += ep_string
log_string += (
f"""{'-' * width}\n"""
f"""{'Total timesteps:':>{pad}} {self.tot_timesteps}\n"""
f"""{'Iteration time:':>{pad}} {iteration_time:.2f}s\n"""
f"""{'Total time:':>{pad}} {self.tot_time:.2f}s\n"""
f"""{'ETA:':>{pad}} {self.tot_time / (locs['it'] + 1) * (
locs['num_learning_iterations'] - locs['it']):.1f}s\n"""
)
print(log_string)
def save(self, path, infos=None):
saved_dict = {
"model_state_dict": self.alg.actor_critic.state_dict(),
"optimizer_state_dict": self.alg.optimizer.state_dict(),
"iter": self.current_learning_iteration,
"infos": infos,
}
if self.empirical_normalization:
saved_dict["obs_norm_state_dict"] = self.obs_normalizer.state_dict()
saved_dict["critic_obs_norm_state_dict"] = self.critic_obs_normalizer.state_dict()
torch.save(saved_dict, path)
# Upload model to external logging service
if self.logger_type in ["neptune", "wandb"]:
self.writer.save_model(path, self.current_learning_iteration)
def load(self, path, load_optimizer=True):
loaded_dict = torch.load(path)
self.alg.actor_critic.load_state_dict(loaded_dict["model_state_dict"])
if self.empirical_normalization:
self.obs_normalizer.load_state_dict(loaded_dict["obs_norm_state_dict"])
self.critic_obs_normalizer.load_state_dict(loaded_dict["critic_obs_norm_state_dict"])
if load_optimizer:
self.alg.optimizer.load_state_dict(loaded_dict["optimizer_state_dict"])
self.current_learning_iteration = loaded_dict["iter"]
return loaded_dict["infos"]
def get_inference_policy(self, device=None):
self.eval_mode() # switch to evaluation mode (dropout for example)
if device is not None:
self.alg.actor_critic.to(device)
policy = self.alg.actor_critic.act_inference
if self.cfg["empirical_normalization"]:
if device is not None:
self.obs_normalizer.to(device)
policy = lambda x: self.alg.actor_critic.act_inference(self.obs_normalizer(x)) # noqa: E731
return policy
def train_mode(self):
self.alg.actor_critic.train()
if self.empirical_normalization:
self.obs_normalizer.train()
self.critic_obs_normalizer.train()
def eval_mode(self):
self.alg.actor_critic.eval()
if self.empirical_normalization:
self.obs_normalizer.eval()
self.critic_obs_normalizer.eval()
def add_git_repo_to_log(self, repo_file_path):
self.git_status_repos.append(repo_file_path)