From af65f2d8ff468412fb324deb0fe08fca39a496b6 Mon Sep 17 00:00:00 2001 From: adaickalavan Date: Tue, 16 May 2023 22:20:21 -0400 Subject: [PATCH] Remove intersection-v0. --- CHANGELOG.md | 1 + README.md | 2 - docs/examples/intersection.md | 4 - docs/examples/rl_model.rst | 1 - examples/rl/intersection/Dockerfile | 44 -- examples/rl/intersection/README.md | 79 --- examples/rl/intersection/config.yaml | 18 - .../intersection/docs/_static/colab-badge.svg | 1 - .../docs/_static/intersection.gif | Bin 600845 -> 0 bytes examples/rl/intersection/intersection.ipynb | 530 -------------- .../rl/intersection/intersection/__init__.py | 0 .../rl/intersection/intersection/action.py | 36 - examples/rl/intersection/intersection/env.py | 41 -- examples/rl/intersection/intersection/info.py | 24 - .../intersection/intersection/observation.py | 24 - .../rl/intersection/intersection/reward.py | 77 -- examples/rl/intersection/requirements.txt | 77 -- examples/rl/intersection/run.py | 154 ---- examples/rl/intersection/setup.py | 27 - smarts/env/__init__.py | 6 - smarts/env/intersection_env.py | 209 ------ smarts/env/tests/test_format_obs.py | 193 ------ smarts/env/wrappers/format_action.py | 126 ---- smarts/env/wrappers/format_obs.py | 656 ------------------ 24 files changed, 1 insertion(+), 2329 deletions(-) delete mode 100644 docs/examples/intersection.md delete mode 100644 examples/rl/intersection/Dockerfile delete mode 100644 examples/rl/intersection/README.md delete mode 100644 examples/rl/intersection/config.yaml delete mode 100644 examples/rl/intersection/docs/_static/colab-badge.svg delete mode 100644 examples/rl/intersection/docs/_static/intersection.gif delete mode 100644 examples/rl/intersection/intersection.ipynb delete mode 100644 examples/rl/intersection/intersection/__init__.py delete mode 100644 examples/rl/intersection/intersection/action.py delete mode 100644 examples/rl/intersection/intersection/env.py delete mode 100644 examples/rl/intersection/intersection/info.py delete mode 100644 examples/rl/intersection/intersection/observation.py delete mode 100644 examples/rl/intersection/intersection/reward.py delete mode 100644 examples/rl/intersection/requirements.txt delete mode 100644 examples/rl/intersection/run.py delete mode 100644 examples/rl/intersection/setup.py delete mode 100644 smarts/env/intersection_env.py delete mode 100644 smarts/env/tests/test_format_obs.py delete mode 100644 smarts/env/wrappers/format_action.py delete mode 100644 smarts/env/wrappers/format_obs.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 2410e8b9b8..c724c49f3d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -29,6 +29,7 @@ Copy and pasting the git commit messages is __NOT__ enough. - `SumoTrafficSimulation` no longer reports that it manages vehicles when it is disconnected. ### Removed - Removed the following dependencies from smarts: `pandas`, `rich`, `twisted`, `sh`. +- Removed `intersection-v0` env and `examples/rl/intersection` as they are superseded by `driving-smarts-v2023` env and `examples/rl/drive`, respectively. ### Security ## [1.1.0] # 2023-04-28 diff --git a/README.md b/README.md index eacde73e4a..4336a95dcc 100644 --- a/README.md +++ b/README.md @@ -46,8 +46,6 @@ Several agent control policies and agent [action types](smarts/core/controllers/ + ActionSpaceType: LaneWithContinuousSpeed ### RL Model -1. [Intersection](examples/rl/intersection) using PPO from [Stable Baselines3](https://github.com/DLR-RM/stable-baselines3). - 1. [Racing](examples/rl/racing) using world model based RL. diff --git a/docs/examples/intersection.md b/docs/examples/intersection.md deleted file mode 100644 index 887497f981..0000000000 --- a/docs/examples/intersection.md +++ /dev/null @@ -1,4 +0,0 @@ -```{include} ../../examples/rl/intersection/README.md -:relative-docs: ../ -:relative-images: -``` \ No newline at end of file diff --git a/docs/examples/rl_model.rst b/docs/examples/rl_model.rst index 929a32eb0a..992903a8d4 100644 --- a/docs/examples/rl_model.rst +++ b/docs/examples/rl_model.rst @@ -6,6 +6,5 @@ RL Model .. toctree:: :maxdepth: 1 - intersection.md racing.md platoon.rst \ No newline at end of file diff --git a/examples/rl/intersection/Dockerfile b/examples/rl/intersection/Dockerfile deleted file mode 100644 index b039430f88..0000000000 --- a/examples/rl/intersection/Dockerfile +++ /dev/null @@ -1,44 +0,0 @@ -FROM pytorch/pytorch:1.7.1-cuda11.0-cudnn8-runtime - -ARG DEBIAN_FRONTEND=noninteractive - -# Install libraries. -RUN apt-get update --fix-missing && \ - apt-get install -y \ - software-properties-common && \ - add-apt-repository -y ppa:deadsnakes/ppa && \ - apt-get update && \ - apt-get install -y \ - git \ - libspatialindex-dev \ - python3.8 \ - python3.8-venv \ - xorg && \ - apt-get autoremove -y && \ - rm -rf /var/lib/apt/lists/* - -# Update default python version. -RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.8 1 - -# Setup virtual environment and install pip. -ENV VIRTUAL_ENV=/opt/.venv -RUN python3.8 -m venv $VIRTUAL_ENV -ENV PATH="$VIRTUAL_ENV/bin:$PATH" -RUN pip install --upgrade pip wheel - -# Install requirements.txt . -COPY ./examples/rl/intersection/requirements.txt /tmp/requirements.txt -RUN pip install --no-cache-dir -r /tmp/requirements.txt - -# Copy source files and install. -COPY . /src -WORKDIR /src -RUN pip install --no-cache-dir -e ./examples/rl/intersection - -# For Envision. -EXPOSE 8081 - -# Suppress message of missing /dev/input folder. -RUN echo "mkdir -p /dev/input" >> ~/.bashrc - -SHELL ["/bin/bash", "-c", "-l"] \ No newline at end of file diff --git a/examples/rl/intersection/README.md b/examples/rl/intersection/README.md deleted file mode 100644 index e325deead0..0000000000 --- a/examples/rl/intersection/README.md +++ /dev/null @@ -1,79 +0,0 @@ -# Intersection -This example illustrates the training of an ego agent to make an unprotected left turn at an all-way-stop intersection with traffic, using the [Stable Baselines3](https://github.com/DLR-RM/stable-baselines3) reinforcement-learning library. Here, the PPO algorithm is used. - -The ego agent earns reward for the distance travelled per-step and is penalised for colliding with other vehicles, for going off-road, for going off-route, or for driving on road-shoulder. - -This example is only meant to demonstrate the use of SB3 library with SMARTS task environments. The trained agent does not solve the task environment. - -## Trained agent making an uprotected left turn -![](./docs/_static/intersection.gif) - -## Observation space -+ Topdown RGB image - + size (width x height): 112 pixels x 112 pixels - + resolution: 50 meter / 112 pixel -``` -observation_space = gym.spaces.Box(low=0, high=255, shape=(3, 112, 112), dtype=np.uint8) -``` - -## Action space -+ Throttle: [0,1] -+ Brake: [0,1] -+ Steering: [-1,1] -``` -action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(3,), dtype=np.float32) -``` - -## Colab -1. Try it here: [![here](./docs/_static/colab-badge.svg)](https://colab.research.google.com/github/huawei-noah/SMARTS/blob/intersection-v0/examples/rl/intersection/intersection.ipynb) - -## Setup -```bash -$ git clone https://github.com/huawei-noah/SMARTS.git -$ cd /SMARTS/examples/rl/intersection -$ python3.8 -m venv ./.venv -$ source ./.venv/bin/activate -$ pip install --upgrade pip -$ pip install -e . -``` - -## Train -1. Train - ```bash - $ cd /SMARTS/examples/rl/intersection - $ python3.8 run.py - ``` -1. Trained model is saved into `/SMARTS/examples/rl/intersection/logs/` folder. -1. Monitor the RL agent during or after the training using tensorboard - ```bash - $ cd /SMARTS/examples/rl/intersection - $ tensorboard --logdir ./logs/ - ``` - -## Evaluate -1. Start - ```bash - $ cd /SMARTS/examples/rl/intersection - $ scl envision start --scenarios ./.venv/lib/python3.8/site-packages/smarts/scenarios & - ``` -1. Run - + Evaluate your own model - ```bash - $ python3.8 run.py --mode=evaluate --model="./logs//" --head - ``` - + Evaluate pre-trained agent - ```bash - $ curl -o ./logs/pretrained/intersection.zip --create-dirs -L https://github.com/Adaickalavan/SMARTS-zoo/raw/main/intersection-v0/PPO_5800000_steps.zip - $ python3.8 run.py --mode=evaluate --model="./logs/pretrained/intersection" --head - ``` -1. Go to `localhost:8081` to view the simulation in Envision. - -## Docker -1. Train a model inside docker - ```bash - $ cd /SMARTS - $ docker build --file=./examples/rl/intersection/Dockerfile --network=host --tag=intersection . - $ docker run --rm -it --network=host --gpus=all intersection - (container) $ cd /src/examples/rl/intersection - (container) $ python3.8 run.py - ``` \ No newline at end of file diff --git a/examples/rl/intersection/config.yaml b/examples/rl/intersection/config.yaml deleted file mode 100644 index e713686049..0000000000 --- a/examples/rl/intersection/config.yaml +++ /dev/null @@ -1,18 +0,0 @@ -smarts: - # Environment - sumo_gui: False # If True, enables sumo-gui display. - img_meters: 50 # Observation image area size in meters. - img_pixels: 112 # Observation image size in pixels. - n_stack: 3 # Number of frames to stack as input to policy network. - - # Training - train_steps: 1e8 # Number of training steps. - checkpoint_freq: 1e5 # Save a model every checkpoint_freq calls to env.step(). - eval_eps: 100 # Number of evaluation epsiodes. 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zif{WTb{~tmk$*wzzt)A%y1Q{WvZ7{q8}s!ua(;?A^KACSBXq&;I@aMuVcOYo>n$Bi zx9k0=Wl}nN;|jz{I5iH=5vt`I|4MWyYxuEQyahHdT=SGvhZeLWCPbt01(j{^xtq$! zc+jyJLvf*%BcFBKf{X&QrKf3d)Gp`{{rpJ?{FwXWT{R$N0_D=2Y2)sRPiG%YKoHek zqgl$^q5W#M=0!YC8gxz%$?W86+Q*M2pE%GtFjZRy^yNEHZjNH(V<`liC6z)j4S~f- z8yp`WL%1iY$j(cjsz}w1ce#(y&WzKIaVkE`iZX5x9>_Pp@>4r)evJyA zw7ZrLqbr8ei%!#HqyU$}+OEx4(Kix`NDs&80LHem?{S01JdZWgg+ zEx5RHHsW@8!G$2CX>NsJT60bJ($0aM=@h=kCc?h-!d$itqQNJa+1%%*kK|}1Seqf# zM97#{TuiyCNHab5B4Kdz`g`v+gM}9Z5i5lB%%`kKvr;5QRsbjF9@NgMOS#6OqdW$N zonACwJ;W7I2QVKW6m@_Se58ceH{aF;FhAvs!+*T6`nLO)!FR%d{ZD?Gdu$BgC!!fn zbbm&SlLkUOTBiPnkc*%fn=?NJA$rM99RcYNPCid>=0d8a)gFG(a5I;R6&+otWw)? zmQn4AUEoF|-4+Q;_2c>ygjT#^kH=by`lIMVUq_AJl1|t;rb1j|Icfh?t}TAMQZ08C zc;bnsetAn8yauh>A7ITOQ_(_?F?=}`YHa&PoLXD}ivV4AK8Nrx8TOB|+B&}{*~)+C t`>0y|d2Y*h^%s|?eH{|h>Yl8qXv+FEX>k->_O*TjJA{tNR-6}|uf diff --git a/examples/rl/intersection/intersection.ipynb b/examples/rl/intersection/intersection.ipynb deleted file mode 100644 index 86856e8345..0000000000 --- a/examples/rl/intersection/intersection.ipynb +++ /dev/null @@ -1,530 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "8gsMrWT3u4WM" - }, - "source": [ - "# Setup\n", - "\n", - "Install the intersection example.\n", - "\n", - "**Note**: The runtime needs to be restarted after installing the dependencies, hence `os.kill(os.getpid(), 9)` is added to stop the current session. Please ignore any resulting error message and simply continue to execute the subsequent cells as per normal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "!git clone https://github.com/huawei-noah/SMARTS /content/SMARTS\n", - "!cd /content/SMARTS && git checkout intersection-v0 && cd /content/SMARTS/examples/rl/intersection && pip install --force-reinstall .\n", - "import sys\n", - "\n", - "sys.path.insert(0, \"/content/SMARTS/\")\n", - "import os\n", - "\n", - "os.kill(os.getpid(), 9)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Develop reinforcement learning code\n", - "\n", - "We begin by building the necessary environment wrappers. Firstly, an info wrapper is built to help log instances when the ego agent succesfully completes an unprotected left turn." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Any, Dict, Tuple\n", - "\n", - "import gym\n", - "\n", - "\n", - "class Info(gym.Wrapper):\n", - " def __init__(self, env: gym.Env):\n", - " super(Info, self).__init__(env)\n", - "\n", - " def step(self, action: Any) -> Tuple[Any, float, bool, Dict[str, Any]]:\n", - " \"\"\"Steps the environment. A new \"is_success\" key is added to the\n", - " returned `info`.\n", - "\n", - " Args:\n", - " action (Any): Action for the agent.\n", - "\n", - " Returns:\n", - " Tuple[ Any, float, bool, Dict[str, Any] ]:\n", - " Observation, reward, done, and info, for the agent is returned.\n", - " \"\"\"\n", - " obs, reward, done, info = self.env.step(action)\n", - " info[\"is_success\"] = bool(info[\"score\"])\n", - "\n", - " return obs, reward, done, info" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `intersection-v0` environment has a continuous action space of `gym.spaces.Box(low=-1.0, high=1.0, shape=(3,), dtype=np.float32)` described by\n", - "+ Throttle: [0,1]\n", - "+ Brake: [0,1]\n", - "+ Steering: [-1,1]\n", - "\n", - "In order to build a simple reinforcement learning policy, we discretise the action space using an action wrapper." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Callable, Tuple\n", - "\n", - "import gym\n", - "import numpy as np\n", - "\n", - "\n", - "class Action(gym.ActionWrapper):\n", - " def __init__(self, env: gym.Env):\n", - " super().__init__(env)\n", - " self._wrapper, self.action_space = _discrete()\n", - "\n", - " def action(self, action):\n", - " \"\"\"Adapts the action input to the wrapped environment.\n", - "\n", - " Note: Users should not directly call this method.\n", - " \"\"\"\n", - " wrapped_act = self._wrapper(action)\n", - " return wrapped_act\n", - "\n", - "\n", - "def _discrete() -> Tuple[Callable[[int], np.ndarray], gym.Space]:\n", - " space = gym.spaces.Discrete(n=4)\n", - "\n", - " action_map = {\n", - " # key: [throttle, brake, steering]\n", - " 0: [0.3, 0, 0], # keep_direction\n", - " 1: [0, 1, 0], # slow_down\n", - " 2: [0.3, 0, -0.5], # turn_left\n", - " 3: [0.3, 0, 0.5], # turn_right\n", - " }\n", - "\n", - " def wrapper(model_action: int) -> np.ndarray:\n", - " throttle, brake, steering = action_map[model_action]\n", - " return np.array([throttle, brake, steering], dtype=np.float32)\n", - "\n", - " return wrapper, space" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we define the rewards using a reward wrapper. The agent is rewarded based on the distance travelled (in meters) per step and is penalised when it collides, goes off-road, goes off-route, goes wrong-way, or drives on the road shoulder." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Dict\n", - "\n", - "import gym\n", - "import numpy as np\n", - "\n", - "\n", - "class Reward(gym.Wrapper):\n", - " def __init__(self, env: gym.Env):\n", - " super().__init__(env)\n", - "\n", - " def reset(self, **kwargs):\n", - " return self.env.reset(**kwargs)\n", - "\n", - " def step(self, action):\n", - " \"\"\"Adapts the wrapped environment's step.\n", - "\n", - " Note: Users should not directly call this method.\n", - " \"\"\"\n", - " obs, reward, done, info = self.env.step(action)\n", - " wrapped_reward = self._reward(obs, reward)\n", - "\n", - " if done:\n", - " if obs[\"events\"][\"reached_goal\"]:\n", - " print(f\"ENV: Hooray! Vehicle reached goal.\")\n", - " elif obs[\"events\"][\"reached_max_episode_steps\"]:\n", - " print(f\"ENV: Vehicle reached max episode steps.\")\n", - " elif (\n", - " obs[\"events\"][\"off_road\"]\n", - " | obs[\"events\"][\"collisions\"]\n", - " | obs[\"events\"][\"off_route\"]\n", - " | obs[\"events\"][\"on_shoulder\"]\n", - " | obs[\"events\"][\"wrong_way\"]\n", - " ):\n", - " pass\n", - " else:\n", - " print(\"Events: \", obs[\"events\"])\n", - " raise Exception(\"Episode ended for unknown reason.\")\n", - "\n", - " return obs, wrapped_reward, done, info\n", - "\n", - " def _reward(self, obs: Dict[str, gym.Space], env_reward: np.float64) -> np.float64:\n", - " reward = 0\n", - "\n", - " # Penalty for driving off road\n", - " if obs[\"events\"][\"off_road\"]:\n", - " reward -= 10\n", - " print(f\"ENV: Vehicle went off road.\")\n", - " return np.float64(reward)\n", - "\n", - " # Penalty for driving on road shoulder\n", - " if obs[\"events\"][\"on_shoulder\"]:\n", - " reward -= 10\n", - " print(f\"ENV: Vehicle went on road shoulder.\")\n", - " return np.float64(reward)\n", - "\n", - " # Penalty for driving on wrong way\n", - " if obs[\"events\"][\"wrong_way\"]:\n", - " reward -= 10\n", - " print(f\"ENV: Vehicle went wrong way.\")\n", - " return np.float64(reward)\n", - "\n", - " # Penalty for colliding\n", - " if obs[\"events\"][\"collisions\"]:\n", - " reward -= 10\n", - " print(f\"ENV: Vehicle collided.\")\n", - " return np.float64(reward)\n", - "\n", - " # Penalty for driving off route\n", - " if obs[\"events\"][\"off_route\"]:\n", - " reward -= 10\n", - " print(f\"ENV: Vehicle went off route.\")\n", - " return np.float64(reward)\n", - "\n", - " # Reward for distance travelled\n", - " reward += env_reward\n", - "\n", - " return np.float64(reward)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The observation space of `intersection-v0` environment is described in `SMARTS/smarts/env/intersection_env.py`. \n", - "\n", - "In this tutorial, only the top-down rgb image from the observation space is used as input to our reinforcement learning policy. Therefore, a wrapper is built to filter the top-down rgb image observation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Dict\n", - "\n", - "import gym\n", - "import numpy as np\n", - "\n", - "\n", - "class Observation(gym.ObservationWrapper):\n", - " def __init__(self, env: gym.Env):\n", - " super().__init__(env)\n", - " old_space = env.observation_space[\"top_down_rgb\"]\n", - " self.observation_space = gym.spaces.Box(\n", - " low=0,\n", - " high=255,\n", - " shape=(old_space.shape[-1],) + old_space.shape[:-1],\n", - " dtype=np.uint8,\n", - " )\n", - "\n", - " def observation(self, obs: Dict[str, gym.Space]) -> np.ndarray:\n", - " top_down_rgb = obs[\"top_down_rgb\"]\n", - "\n", - " # Channel first\n", - " top_down_rgb = top_down_rgb.transpose(2, 0, 1)\n", - "\n", - " return np.uint8(top_down_rgb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We proceed to make the `intersection-v0` environment and wrap it with the previously built wrappers. The environment is additionally wrapped with `VecFrameStack`, from Stable Baselines3 library, to give a sense of time to the policy network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from stable_baselines3.common.env_checker import check_env\n", - "from stable_baselines3.common.vec_env import DummyVecEnv, VecFrameStack, VecMonitor\n", - "\n", - "\n", - "def make_env(config: Dict[str, Any]) -> gym.Env:\n", - " # Create environment\n", - " env = gym.make(\n", - " \"smarts.env:intersection-v0\",\n", - " headless=True,\n", - " sumo_headless=True,\n", - " img_meters=config[\"img_meters\"],\n", - " img_pixels=config[\"img_pixels\"],\n", - " )\n", - "\n", - " # Wrap env with action, reward, and observation wrapper\n", - " env = Info(env=env)\n", - " env = Action(env=env)\n", - " env = Reward(env=env)\n", - " env = Observation(env=env)\n", - "\n", - " # Check custom environment\n", - " check_env(env)\n", - "\n", - " # Wrap env with SB3 wrappers\n", - " env = DummyVecEnv([lambda: env])\n", - " env = VecFrameStack(venv=env, n_stack=config[\"n_stack\"], channels_order=\"first\")\n", - " env = VecMonitor(\n", - " venv=env,\n", - " filename=str(config[\"logdir\"]),\n", - " info_keywords=(\"is_success\",),\n", - " )\n", - "\n", - " return env" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3v0xcpPfv6L3" - }, - "source": [ - "The training and evaluation code is prepared using the Stable Baseline3 API. Training progress is checkpointed using a callback. At the end, the trained agent is saved and evaluated. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "DWsQgj0NvGNt" - }, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "from typing import Any, Dict\n", - "\n", - "import gym\n", - "import stable_baselines3 as sb3lib\n", - "from stable_baselines3.common.callbacks import CheckpointCallback, EvalCallback\n", - "from stable_baselines3.common.evaluation import evaluate_policy\n", - "\n", - "\n", - "def run(env: gym.Env, eval_env: gym.Env, config: Dict[str, Any]):\n", - "\n", - " checkpoint_callback = CheckpointCallback(\n", - " save_freq=config[\"checkpoint_freq\"],\n", - " save_path=config[\"logdir\"] / \"checkpoint\",\n", - " name_prefix=config[\"alg\"],\n", - " )\n", - " eval_callback = EvalCallback(\n", - " eval_env=eval_env,\n", - " n_eval_episodes=config[\"eval_eps\"],\n", - " eval_freq=config[\"eval_freq\"],\n", - " log_path=config[\"logdir\"] / \"eval\",\n", - " best_model_save_path=config[\"logdir\"] / \"eval\",\n", - " deterministic=True,\n", - " )\n", - "\n", - " if config[\"mode\"] == \"evaluate\":\n", - " print(\"\\nStart evaluation.\\n\")\n", - " model = getattr(sb3lib, config[\"alg\"]).load(\n", - " config[\"model\"], print_system_info=True\n", - " )\n", - " else:\n", - " print(\"\\nStart training from scratch.\\n\")\n", - " model = getattr(sb3lib, config[\"alg\"])(\n", - " env=env,\n", - " verbose=1,\n", - " tensorboard_log=config[\"logdir\"] / \"tensorboard\",\n", - " **config[\"alg_kwargs\"],\n", - " )\n", - " model.learn(\n", - " total_timesteps=config[\"train_steps\"],\n", - " callback=[checkpoint_callback, eval_callback],\n", - " )\n", - " save_dir = config[\"logdir\"] / \"train\"\n", - " save_dir.mkdir(parents=True, exist_ok=True)\n", - " time = datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S\")\n", - " model.save(save_dir / (\"model_\" + time))\n", - " print(\"\\nSaved trained model.\\n\")\n", - "\n", - " print(\"\\nEvaluate policy.\\n\")\n", - " mean_reward, std_reward = evaluate_policy(\n", - " model, eval_env, n_eval_episodes=config[\"eval_eps\"], deterministic=True\n", - " )\n", - " print(f\"Mean reward:{mean_reward:.2f} +/- {std_reward:.2f}\")\n", - " print(\"\\nFinished evaluating.\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We specify several training prameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "warnings.simplefilter(\"ignore\", category=DeprecationWarning)\n", - "warnings.simplefilter(\"ignore\", category=ResourceWarning)\n", - "\n", - "config = {\n", - " \"img_meters\": 50, # Observation image area size in meters.\n", - " \"img_pixels\": 112, # Observation image size in pixels.\n", - " \"n_stack\": 3, # Number of frames to stack as input to policy network.\n", - " \"train_steps\": 2500, # Number of training steps.\n", - " \"checkpoint_freq\": 1e3, # Save a model every checkpoint_freq calls to env.step().\n", - " \"eval_eps\": 10, # Number of evaluation epsiodes.\n", - " \"eval_freq\": 1e3, # Evaluate the trained model every eval_freq steps and save the best model.\n", - " \"alg\": \"PPO\", # Stable Baselines3 algorithm.\n", - " \"alg_kwargs\": {\"policy\": \"CnnPolicy\", \"target_kl\": 0.1}, # Network policy.\n", - "}\n", - "\n", - "\n", - "def main(config):\n", - " # Make training and evaluation environments.\n", - " env = make_env(config=config)\n", - " eval_env = make_env(config=config)\n", - "\n", - " # Run training or evaluation.\n", - " run(env=env, eval_env=eval_env, config=config)\n", - " env.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we are ready to train the agent." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "from pathlib import Path\n", - "\n", - "time = datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S\")\n", - "logdir = Path(\"/content/SMARTS/examples/rl/intersection/logs\") / time\n", - "logdir.mkdir(parents=True, exist_ok=True)\n", - "config[\"logdir\"] = logdir\n", - "config[\"mode\"] = \"train\"\n", - "\n", - "# Train the agent\n", - "main(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Evaluate a pre-trained agent\n", - "\n", - "Evaluate a pre-trained agent and compare its performance with the newly-trained agent above. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the pre-trained agent.\n", - "!curl -o /content/SMARTS/examples/rl/intersection/logs/pretrained/intersection.zip --create-dirs -L https://github.com/Adaickalavan/SMARTS-zoo/raw/main/intersection-v0/PPO_5800000_steps.zip\n", - "\n", - "time = datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S\")\n", - "logdir = Path(\"/content/SMARTS/examples/rl/intersection/logs\") / time\n", - "logdir.mkdir(parents=True, exist_ok=True)\n", - "config[\"logdir\"] = logdir\n", - "config[\"mode\"] = \"evaluate\"\n", - "config[\n", - " \"model\"\n", - "] = \"/content/SMARTS/examples/rl/intersection/logs/pretrained/intersection\"\n", - "\n", - "# Evaluate the pre-trained agent\n", - "main(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tensorboard\n", - "\n", - "For reference, you may want to view the training logs captured during the training of the pre-trained agent." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download the tensorboard logs of the pre-trained agent\n", - "!curl -o /content/SMARTS/examples/rl/intersection/logs/pretrained/events.out.tfevents.1651601587.gx3.16.0 --create-dirs -L https://github.com/Adaickalavan/SMARTS-zoo/raw/main/intersection-v0/events.out.tfevents.1651601587.gx3.16.0\n", - "\n", - "# Load the TensorBoard extension\n", - "%load_ext tensorboard\n", - "%tensorboard --logdir /content/SMARTS/examples/rl/intersection/logs/pretrained/" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "name": "sb3_example.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.8.10" - }, - "vscode": { - "interpreter": { - "hash": "983a288b1deae516b5d1a3268f286490dc1a3bd215a042403a142fb3df5f8acd" - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/rl/intersection/intersection/__init__.py b/examples/rl/intersection/intersection/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/examples/rl/intersection/intersection/action.py b/examples/rl/intersection/intersection/action.py deleted file mode 100644 index 515c085877..0000000000 --- a/examples/rl/intersection/intersection/action.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Callable, Tuple - -import gym -import numpy as np - - -class Action(gym.ActionWrapper): - def __init__(self, env: gym.Env): - super().__init__(env) - self._wrapper, self.action_space = _discrete() - - def action(self, action): - """Adapts the action input to the wrapped environment. - - Note: Users should not directly call this method. - """ - wrapped_act = self._wrapper(action) - return wrapped_act - - -def _discrete() -> Tuple[Callable[[int], np.ndarray], gym.Space]: - space = gym.spaces.Discrete(n=4) - - action_map = { - # key: [throttle, brake, steering] - 0: [0.3, 0, 0], # keep_direction - 1: [0, 1, 0], # slow_down - 2: [0.3, 0, -0.5], # turn_left - 3: [0.3, 0, 0.5], # turn_right - } - - def wrapper(model_action: int) -> np.ndarray: - throttle, brake, steering = action_map[model_action] - return np.array([throttle, brake, steering], dtype=np.float32) - - return wrapper, space diff --git a/examples/rl/intersection/intersection/env.py b/examples/rl/intersection/intersection/env.py deleted file mode 100644 index b8663b1817..0000000000 --- a/examples/rl/intersection/intersection/env.py +++ /dev/null @@ -1,41 +0,0 @@ -from typing import Any, Dict - -import gym -from intersection import action as intersection_action -from intersection import info as intersection_info -from intersection import observation as intersection_observation -from intersection import reward as intersection_reward -from stable_baselines3.common.env_checker import check_env -from stable_baselines3.common.vec_env import DummyVecEnv, VecFrameStack, VecMonitor - - -def make(config: Dict[str, Any]) -> gym.Env: - # Create environment - env = gym.make( - "smarts.env:intersection-v0", - headless=not config["head"], # If False, enables Envision display. - visdom=config["visdom"], # If True, enables Visdom display. - sumo_headless=not config["sumo_gui"], # If False, enables sumo-gui display. - img_meters=config["img_meters"], - img_pixels=config["img_pixels"], - ) - - # Wrap env with action, reward, and observation wrapper - env = intersection_info.Info(env=env) - env = intersection_action.Action(env=env) - env = intersection_reward.Reward(env=env) - env = intersection_observation.Observation(env=env) - - # Check custom environment - check_env(env) - - # Wrap env with SB3 wrappers - env = DummyVecEnv([lambda: env]) - env = VecFrameStack(venv=env, n_stack=config["n_stack"], channels_order="first") - env = VecMonitor( - venv=env, - filename=str(config["logdir"]), - info_keywords=("is_success",), - ) - - return env diff --git a/examples/rl/intersection/intersection/info.py b/examples/rl/intersection/intersection/info.py deleted file mode 100644 index 58f509b4d2..0000000000 --- a/examples/rl/intersection/intersection/info.py +++ /dev/null @@ -1,24 +0,0 @@ -from typing import Any, Dict, Tuple - -import gym - - -class Info(gym.Wrapper): - def __init__(self, env: gym.Env): - super(Info, self).__init__(env) - - def step(self, action: Any) -> Tuple[Any, float, bool, Dict[str, Any]]: - """Steps the environment. A new "is_success" key is added to the - returned `info`. - - Args: - action (Any): Action for the agent. - - Returns: - Tuple[ Any, float, bool, Dict[str, Any] ]: - Observation, reward, done, and info, for the agent is returned. - """ - obs, reward, done, info = self.env.step(action) - info["is_success"] = bool(info["score"]) - - return obs, reward, done, info diff --git a/examples/rl/intersection/intersection/observation.py b/examples/rl/intersection/intersection/observation.py deleted file mode 100644 index 7e21b0a50e..0000000000 --- a/examples/rl/intersection/intersection/observation.py +++ /dev/null @@ -1,24 +0,0 @@ -from typing import Dict - -import gym -import numpy as np - - -class Observation(gym.ObservationWrapper): - def __init__(self, env: gym.Env): - super().__init__(env) - old_space = env.observation_space["top_down_rgb"] - self.observation_space = gym.spaces.Box( - low=0, - high=255, - shape=(old_space.shape[-1],) + old_space.shape[:-1], - dtype=np.uint8, - ) - - def observation(self, obs: Dict[str, gym.Space]) -> np.ndarray: - top_down_rgb = obs["top_down_rgb"] - - # Channel first - top_down_rgb = top_down_rgb.transpose(2, 0, 1) - - return np.uint8(top_down_rgb) diff --git a/examples/rl/intersection/intersection/reward.py b/examples/rl/intersection/intersection/reward.py deleted file mode 100644 index 523921336f..0000000000 --- a/examples/rl/intersection/intersection/reward.py +++ /dev/null @@ -1,77 +0,0 @@ -from typing import Dict - -import gym -import numpy as np - - -class Reward(gym.Wrapper): - def __init__(self, env: gym.Env): - super().__init__(env) - - def reset(self, **kwargs): - return self.env.reset(**kwargs) - - def step(self, action): - """Adapts the wrapped environment's step. - - Note: Users should not directly call this method. - """ - obs, reward, done, info = self.env.step(action) - wrapped_reward = self._reward(obs, reward) - - if done: - if obs["events"]["reached_goal"]: - print(f"ENV: Hooray! Vehicle reached goal.") - elif obs["events"]["reached_max_episode_steps"]: - print(f"ENV: Vehicle reached max episode steps.") - elif ( - obs["events"]["off_road"] - | obs["events"]["collisions"] - | obs["events"]["off_route"] - | obs["events"]["on_shoulder"] - | obs["events"]["wrong_way"] - ): - pass - else: - print("Events: ", obs["events"]) - raise Exception("Episode ended for unknown reason.") - - return obs, wrapped_reward, done, info - - def _reward(self, obs: Dict[str, gym.Space], env_reward: np.float64) -> np.float64: - reward = 0 - - # Penalty for driving off road - if obs["events"]["off_road"]: - reward -= 10 - print(f"ENV: Vehicle went off road.") - return np.float64(reward) - - # Penalty for driving on road shoulder - if obs["events"]["on_shoulder"]: - reward -= 10 - print(f"ENV: Vehicle went on road shoulder.") - return np.float64(reward) - - # Penalty for driving on wrong way - if obs["events"]["wrong_way"]: - reward -= 10 - print(f"ENV: Vehicle went wrong way.") - return np.float64(reward) - - # Penalty for colliding - if obs["events"]["collisions"]: - reward -= 10 - print(f"ENV: Vehicle collided.") - return np.float64(reward) - - # Penalty for driving off route - if obs["events"]["off_route"]: - reward -= 10 - print(f"ENV: Vehicle went off route.") - return np.float64(reward) - - # Reward for distance travelled - reward += env_reward - - return np.float64(reward) diff --git a/examples/rl/intersection/requirements.txt b/examples/rl/intersection/requirements.txt deleted file mode 100644 index 7dac629b9f..0000000000 --- a/examples/rl/intersection/requirements.txt +++ /dev/null @@ -1,77 +0,0 @@ -absl-py==1.0.0 -attrs==21.4.0 -Automat==20.2.0 -cached-property==1.5.2 -cachetools==4.2.4 -certifi==2022.5.18.1 -charset-normalizer==2.0.12 -click==8.0.4 -cloudpickle==1.3.0 -commonmark==0.9.1 -constantly==15.1.0 -cycler==0.11.0 -eclipse-sumo==1.10.0 -fonttools==4.33.3 -future==0.18.2 -google-auth==1.35.0 -google-auth-oauthlib==0.4.6 -grpcio==1.32.0 -gym==0.19.0 -hyperlink==21.0.0 -idna==3.3 -ijson==3.1.4 -importlib-metadata==4.11.4 -incremental==21.3.0 -jsonpatch==1.32 -jsonpointer==2.3 -kiwisolver==1.4.2 -Markdown==3.3.7 -matplotlib==3.5.2 -numpy==1.21.6 -oauthlib==3.2.0 -packaging==21.3 -Panda3D==1.10.9 -panda3d-gltf==0.13 -panda3d-simplepbr==0.10 -pandas==1.3.5 -Pillow==9.1.1 -protobuf==3.20.1 -psutil==5.9.1 -pyasn1==0.4.8 -pyasn1-modules==0.2.8 -pybullet==3.0.6 -Pygments==2.12.0 -pyparsing==3.0.9 -python-dateutil==2.8.2 -pytz==2022.1 -PyYAML==6.0 -pyzmq==23.0.0 -requests==2.27.1 -requests-oauthlib==1.3.1 -rich==12.4.4 -rsa==4.8 -Rtree==1.0.0 -ruamel.yaml==0.17.17 -ruamel.yaml.clib==0.2.6 -scipy==1.7.3 -sh==1.14.2 -Shapely==1.8.2 -six==1.16.0 -stable-baselines3==1.4.0 -tableprint==0.9.1 -tensorboard==2.2.0 -tensorboard-plugin-wit==1.8.1 -torch==1.11.0 -torchfile==0.1.0 -tornado==6.1 -trimesh==3.9.29 -Twisted==22.4.0 -typing_extensions==4.2.0 -urllib3==1.26.9 -visdom==0.1.8.9 -wcwidth==0.2.5 -websocket-client==1.3.2 -Werkzeug==2.1.2 -yattag==1.14.0 -zipp==3.8.0 -zope.interface==5.4.0 diff --git a/examples/rl/intersection/run.py b/examples/rl/intersection/run.py deleted file mode 100644 index cf3c99cb14..0000000000 --- a/examples/rl/intersection/run.py +++ /dev/null @@ -1,154 +0,0 @@ -import os - -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" -import argparse -import warnings -from datetime import datetime -from pathlib import Path -from typing import Any, Dict - -import gym -import stable_baselines3 as sb3lib -import torch as th -from intersection import env as intersection_env -from ruamel.yaml import YAML -from stable_baselines3.common.callbacks import CheckpointCallback, EvalCallback -from stable_baselines3.common.evaluation import evaluate_policy - -print("\nTorch cuda is available: ", th.cuda.is_available(), "\n") -warnings.simplefilter("ignore", category=DeprecationWarning) -warnings.simplefilter("ignore", category=ResourceWarning) -yaml = YAML(typ="safe") - - -def main(args: argparse.Namespace): - # Load config file. - config_file = yaml.load( - (Path(__file__).absolute().parent / "config.yaml").read_text() - ) - - # Load env config. - config = config_file["smarts"] - config["head"] = args.head - config["mode"] = args.mode - - # Setup logdir. - if not args.logdir: - time = datetime.now().strftime("%Y_%m_%d_%H_%M_%S") - logdir = Path(__file__).absolute().parents[0] / "logs" / time - else: - logdir = Path(args.logdir) - logdir.mkdir(parents=True, exist_ok=True) - config["logdir"] = logdir - print("\nLogdir:", logdir, "\n") - - # Setup model. - if (config["mode"] == "train" and args.model) or (config["mode"] == "evaluate"): - # Begin training or evaluation from a pre-trained agent. - config["model"] = args.model - print("\nModel:", config["model"], "\n") - elif config["mode"] == "train" and not args.model: - # Begin training from scratch. - pass - else: - raise KeyError(f'Expected \'train\' or \'evaluate\', but got {config["mode"]}.') - - # Make training and evaluation environments. - env = intersection_env.make(config=config) - eval_env = intersection_env.make(config=config) - - # Run training or evaluation. - run(env=env, eval_env=eval_env, config=config) - env.close() - - -def run(env: gym.Env, eval_env: gym.Env, config: Dict[str, Any]): - - checkpoint_callback = CheckpointCallback( - save_freq=config["checkpoint_freq"], - save_path=config["logdir"] / "checkpoint", - name_prefix=config["alg"], - ) - eval_callback = EvalCallback( - eval_env=eval_env, - n_eval_episodes=config["eval_eps"], - eval_freq=config["eval_freq"], - log_path=config["logdir"] / "eval", - best_model_save_path=config["logdir"] / "eval", - deterministic=True, - ) - - if config["mode"] == "evaluate": - print("\nStart evaluation.\n") - model = getattr(sb3lib, config["alg"]).load( - config["model"], print_system_info=True - ) - elif config["mode"] == "train" and config.get("model", None): - print("\nStart training from an existing model.\n") - model = getattr(sb3lib, config["alg"]).load( - config["model"], print_system_info=True - ) - model.set_env(env) - model.learn( - total_timesteps=config["train_steps"], - callback=[checkpoint_callback, eval_callback], - ) - else: - print("\nStart training from scratch.\n") - model = getattr(sb3lib, config["alg"])( - env=env, - verbose=1, - tensorboard_log=config["logdir"] / "tensorboard", - **config["alg_kwargs"], - ) - model.learn( - total_timesteps=config["train_steps"], - callback=[checkpoint_callback, eval_callback], - ) - - if config["mode"] == "train": - save_dir = config["logdir"] / "train" - save_dir.mkdir(parents=True, exist_ok=True) - time = datetime.now().strftime("%Y_%m_%d_%H_%M_%S") - model.save(save_dir / ("model_" + time)) - print("\nSaved trained model.\n") - - print("\nEvaluate policy.\n") - mean_reward, std_reward = evaluate_policy( - model, eval_env, n_eval_episodes=config["eval_eps"], deterministic=True - ) - print(f"Mean reward:{mean_reward:.2f} +/- {std_reward:.2f}") - print("\nFinished evaluating.\n") - - -if __name__ == "__main__": - program = Path(__file__).stem - parser = argparse.ArgumentParser(program) - parser.add_argument( - "--mode", - help="`train` or `evaluate`. Default is `train`.", - type=str, - default="train", - ) - parser.add_argument( - "--logdir", - help="Directory path for saving logs. Required if `--mode=evaluate`, else optional.", - type=str, - default=None, - ) - parser.add_argument( - "--model", - help="Directory path to saved RL model. Required if `--mode=evaluate`, else optional.", - type=str, - default=None, - ) - parser.add_argument( - "--head", help="Run the simulation with display.", action="store_true" - ) - - args = parser.parse_args() - - if args.mode == "evaluate" and args.model is None: - raise Exception("When --mode=evaluate, --model option must be specified.") - - main(args) diff --git a/examples/rl/intersection/setup.py b/examples/rl/intersection/setup.py deleted file mode 100644 index eaf1447967..0000000000 --- a/examples/rl/intersection/setup.py +++ /dev/null @@ -1,27 +0,0 @@ -from os import path - -from setuptools import find_packages, setup - -this_dir = path.abspath(path.dirname(__file__)) -with open(path.join(this_dir, "README.md"), encoding="utf-8") as f: - long_description = f.read() - -setup( - name="intersection", - description="Unprotected left turn intersection", - long_description=long_description, - long_description_content_type="text/markdown", - version="0.1.0", - packages=find_packages(), - include_package_data=True, - zip_safe=True, - python_requires=">=3.8", - install_requires=[ - "setuptools>=41.0.0,!=50.0", - "protobuf==3.20.1", - "ruamel.yaml==0.17.17", - "smarts[camera-obs] @ git+https://github.com/huawei-noah/SMARTS.git@intersection-v0", - "stable-baselines3==1.4.0", - "tensorboard==2.2.0", - ], -) diff --git a/smarts/env/__init__.py b/smarts/env/__init__.py index 899277d208..707acddb4a 100644 --- a/smarts/env/__init__.py +++ b/smarts/env/__init__.py @@ -25,11 +25,5 @@ entry_point="smarts.env.hiway_env:HiWayEnv", ) -register( - id="intersection-v0", - entry_point="smarts.env.intersection_env:intersection_v0_env", -) - - # Do NOT remove. import smarts.env.gymnasium diff --git a/smarts/env/intersection_env.py b/smarts/env/intersection_env.py deleted file mode 100644 index cbc8db2ac5..0000000000 --- a/smarts/env/intersection_env.py +++ /dev/null @@ -1,209 +0,0 @@ -# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. - -import pathlib -from typing import Any, Dict, Optional, Tuple - -import gym - -from smarts.core.agent_interface import ( - OGM, - RGB, - AgentInterface, - DoneCriteria, - DrivableAreaGridMap, - NeighborhoodVehicles, - Waypoints, -) -from smarts.core.controllers import ActionSpaceType -from smarts.env.hiway_env import HiWayEnv -from smarts.env.wrappers.format_action import FormatAction -from smarts.env.wrappers.format_obs import FormatObs -from smarts.env.wrappers.single_agent import SingleAgent -from smarts.sstudio.scenario_construction import build_scenarios -from smarts.zoo.agent_spec import AgentSpec - - -def intersection_v0_env( - headless: bool = True, - visdom: bool = False, - sumo_headless: bool = True, - envision_record_data_replay_path: Optional[str] = None, - img_meters: int = 64, - img_pixels: int = 256, -): - """An intersection environment where a single agent needs to make an - unprotected left turn in the presence of traffic and without traffic - lights. Traffic vehicles stop before entering the junction. - - **Observation** - - A ``smarts.env.wrappers.format_obs:StdObs`` dict, containing enabled keys, is returned as observation. - - **Actions** - - Type ``gym.spaces.Box(low=-1.0, high=1.0, shape(3,), dtype=np.float32)`` - - +--------------+-------------+ - | Action | Value range | - +==============+=============+ - | Throttle | [ 0, 1] | - +--------------+-------------+ - | Break | [ 0, 1] | - +--------------+-------------+ - | Steering | [-1, 1] | - +--------------+-------------+ - - **Reward** - - Reward is distance travelled (in meters) in each step, including the - termination step. - - **Episode termination** - - Episode is terminated if any of the following occurs. - - + Steps per episode exceed 3000. - + Agent collides, drives off road, drives off route, drives on shoulder, - or drives on wrong way. - - **Solved requirement** - - If agent successfully navigates the intersection then ``info["score"]`` - will equal 1, else it is 0. Considered solved when ``info["score"] == 1`` - is achieved over 800 consecutive episodes. - - Args: - headless (bool, optional): If True, disables visualization in - Envision. Defaults to False. - visdom (bool, optional): If True, enables visualization of observed - RGB images in Visdom. Defaults to False. - sumo_headless (bool, optional): If True, disables visualization in - SUMO GUI. Defaults to True. - envision_record_data_replay_path (Optional[str], optional): - Envision's data replay output directory. Defaults to None. - img_meters (int): Ground square size covered by image observations. - Defaults to 64 x 64 meter (height x width) square. - img_pixels (int): Pixels representing the square image observations. - Defaults to 256 x 256 pixels (height x width) square. - - Returns: - A single-agent unprotected left turn intersection environment. - """ - - scenarios = [ - str( - pathlib.Path(__file__).absolute().parents[2] - / "scenarios" - / "sumo" - / "intersections" - / "1_to_1lane_left_turn_c_agents_1" - ) - ] - build_scenarios(scenarios=scenarios) - - done_criteria = DoneCriteria( - collision=True, - off_road=True, - off_route=True, - on_shoulder=True, - wrong_way=True, - not_moving=False, - agents_alive=None, - ) - max_episode_steps = 3000 - agent_specs = { - "LeftTurnAgent": AgentSpec( - interface=AgentInterface( - accelerometer=True, - action=ActionSpaceType.Continuous, - done_criteria=done_criteria, - drivable_area_grid_map=DrivableAreaGridMap( - width=img_pixels, - height=img_pixels, - resolution=img_meters / img_pixels, - ), - lidar_point_cloud=True, - max_episode_steps=max_episode_steps, - neighborhood_vehicle_states=NeighborhoodVehicles(img_meters), - occupancy_grid_map=OGM( - width=img_pixels, - height=img_pixels, - resolution=img_meters / img_pixels, - ), - top_down_rgb=RGB( - width=img_pixels, - height=img_pixels, - resolution=img_meters / img_pixels, - ), - road_waypoints=False, - waypoint_paths=Waypoints(lookahead=img_meters), - ), - ) - } - - env = HiWayEnv( - scenarios=scenarios, - agent_specs=agent_specs, - sim_name="LeftTurn", - headless=headless, - visdom=visdom, - sumo_headless=sumo_headless, - envision_record_data_replay_path=envision_record_data_replay_path, - ) - env = FormatObs(env=env) - env = FormatAction(env=env, space=ActionSpaceType.Continuous) - env = _InfoScore(env=env) - env = SingleAgent(env=env) - - return env - - -class _InfoScore(gym.Wrapper): - def __init__(self, env: gym.Env): - super(_InfoScore, self).__init__(env) - - def step( - self, action: Dict[str, Any] - ) -> Tuple[ - Dict[str, Any], - Dict[str, float], - Dict[str, bool], - Dict[str, Dict[str, Any]], - ]: - """Steps the environment. A modified `score` is added to the returned - `info` of each agent. - - Args: - action (Dict[str, Any]): Action for each agent. - - Returns: - Tuple[ Dict[str, Any], Dict[str, float], Dict[str, bool], Dict[str, Dict[str, Any]] ]: - Observation, reward, done, and info, for each agent is returned. - """ - obs, reward, done, info = self.env.step(action) - - for agent_id in obs.keys(): - reached_goal = obs[agent_id]["events"]["reached_goal"] - # Set `score=1` if ego agent successfully navigates the left-turn - # intersection and reaches the end of mission route, else `score=0`. - info[agent_id]["score"] = reached_goal - - return obs, reward, done, info diff --git a/smarts/env/tests/test_format_obs.py b/smarts/env/tests/test_format_obs.py deleted file mode 100644 index 5ddee1c44e..0000000000 --- a/smarts/env/tests/test_format_obs.py +++ /dev/null @@ -1,193 +0,0 @@ -# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. - -import dataclasses - -import gym -import pytest - -from smarts.core.agent import Agent -from smarts.core.agent_interface import ( - AgentInterface, - NeighborhoodVehicles, - Signals, - Waypoints, -) -from smarts.core.controllers import ActionSpaceType -from smarts.env.wrappers.format_obs import FormatObs, intrfc_to_stdobs -from smarts.zoo.agent_spec import AgentSpec - - -def _intrfcs_init(): - return [ - [{"accelerometer": True}, {"accelerometer": False}], - [{"accelerometer": True}] * 2, - [{"drivable_area_grid_map": True}] * 2, - [{"lidar_point_cloud": True}] * 2, - [{"neighborhood_vehicle_states": True}] * 2, - [{"occupancy_grid_map": True}] * 2, - [{"top_down_rgb": True}] * 2, - [{"waypoint_paths": Waypoints(lookahead=1)}] * 2, - [ - { - "neighborhood_vehicle_states": True, - "waypoint_paths": Waypoints(lookahead=1), - } - ] - * 2, - [{"signals": Signals(lookahead=100.0)}] * 2, - ] - - -def _intrfcs_obs(): - base_intrfc = { - "accelerometer": True, - "drivable_area_grid_map": True, - "lidar_point_cloud": True, - "neighborhood_vehicle_states": True, - "occupancy_grid_map": True, - "top_down_rgb": True, - "waypoint_paths": Waypoints(lookahead=1), - "signals": Signals(lookahead=100), - } - - return [ - [base_intrfc] * 2, - [ - dict( - base_intrfc, - **{"neighborhood_vehicle_states": NeighborhoodVehicles(radius=0)} - ) - ] - * 2, - [dict(base_intrfc, **{"accelerometer": False})] * 2, - [dict(base_intrfc, **{"waypoint_paths": Waypoints(lookahead=50)})] * 2, - [dict(base_intrfc, **{"signals": Signals(lookahead=50)})] * 2, - ] - - -def _make_agent_specs(intrfcs): - base_intrfc = AgentInterface( - action=ActionSpaceType.Lane, - accelerometer=False, - drivable_area_grid_map=False, - lidar_point_cloud=False, - neighborhood_vehicle_states=False, - occupancy_grid_map=False, - top_down_rgb=False, - waypoint_paths=False, - signals=False, - ) - - return { - "AGENT_" - + agent_id: AgentSpec( - interface=dataclasses.replace(base_intrfc, **intrfc), - agent_builder=lambda: Agent.from_function(lambda _: "keep_lane"), - ) - for agent_id, intrfc in zip(["001", "002"], intrfcs) - } - - -@pytest.fixture -def make_env(request): - env = gym.make( - "smarts.env:hiway-v0", - scenarios=["scenarios/sumo/figure_eight"], - agent_specs=_make_agent_specs(request.param), - headless=True, - ) - - yield env, request.param - env.close() - - -@pytest.mark.parametrize("make_env", _intrfcs_init(), indirect=True) -def test_init(make_env): - base_env, cur_intrfcs = make_env - - # Test wrapping an env with non-identical agent interfaces - base_intrfcs = [interface for interface in base_env.agent_interfaces.values()] - if not all(intrfc == base_intrfcs[0] for intrfc in base_intrfcs): - with pytest.raises(AssertionError): - env = FormatObs(env=base_env) - env.close() - return - - env = FormatObs(env=base_env) - - # Test whether observation space of wrapped env is created properly - agent_id = next(iter(base_env.agent_specs.keys())) - rcv_space = env.observation_space - rcv_space_keys = set([key for key in rcv_space[agent_id]]) - - des_space_keys = set( - ["distance_travelled", "ego_vehicle_state", "events", "mission"] - ) - opt_space_keys = [ - intrfc_to_stdobs(intrfc) - for intrfc, val in cur_intrfcs[0].items() - if val and intrfc_to_stdobs(intrfc) - ] - des_space_keys.update(opt_space_keys) - - assert rcv_space_keys == des_space_keys - - # Test accelerometer space in observation space of wrapped env - des_ego_keys = set( - ["angular_acceleration", "angular_jerk", "linear_acceleration", "linear_jerk"] - ) - rcv_ego_keys = set([key for key in rcv_space[agent_id]["ego_vehicle_state"]]) - if cur_intrfcs[0].get("accelerometer", None): - assert des_ego_keys.issubset(rcv_ego_keys) - else: - assert des_ego_keys.isdisjoint(rcv_ego_keys) - - env.close() - - -def _check_observation( - rcv_space, - obs, -): - for field1, val1 in obs.items(): - if isinstance(val1, dict): - for field2, val2 in val1.items(): - assert val2.shape == rcv_space[field1][field2].shape - assert val2.dtype == rcv_space[field1][field2].dtype - else: - assert val1.shape == rcv_space[field1].shape - assert val1.dtype == rcv_space[field1].dtype - - return - - -@pytest.mark.parametrize("make_env", _intrfcs_obs(), indirect=True) -def test_observation(make_env): - base_env, _ = make_env - env = FormatObs(env=base_env) - rcv_space = env.observation_space - - # Test whether returned observation matches observation space - obs = env.reset() - for agent_id, ob in obs.items(): - _check_observation(rcv_space[agent_id], ob) - - env.close() diff --git a/smarts/env/wrappers/format_action.py b/smarts/env/wrappers/format_action.py deleted file mode 100644 index a9caac086a..0000000000 --- a/smarts/env/wrappers/format_action.py +++ /dev/null @@ -1,126 +0,0 @@ -# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. - -from typing import Callable, Dict, Tuple - -import gym -import numpy as np - -from smarts.core.controllers import ActionSpaceType - - -class FormatAction(gym.ActionWrapper): - """Sets gym-compliant action space for SMARTS environment. - - Note: - - (a) Only ``ActionSpaceType.Continuous``, ``ActionSpaceType.Lane``, - ``ActionSpaceType.ActuatorDynamic``, and `ActionSpaceType.TargetPose`` - are supported by this wrapper now. - - (b) All agents should have the same action space. - """ - - def __init__(self, env: gym.Env, space: ActionSpaceType): - """Sets identical action space, denoted by ``space``, for all agents. - - :param env: Gym env to be wrapped. - :type env: class: gym.Env - :param space: Denotes the desired action space type from - ``smarts.core.controllers.ActionSpaceType``. - :type space: str - """ - super().__init__(env) - space_map = { - "Continuous": _continuous, - "Lane": _lane, - "ActuatorDynamic": _actuator_dynamic, - "TargetPose": _target_pose, - } - self._wrapper, action_space = space_map.get(space.name)() - - self.action_space = gym.spaces.Dict( - {agent_id: action_space for agent_id in self.agent_interfaces.keys()} - ) - - def action(self, action): - """Adapts the action input to the wrapped environment. - - Note: Users should not directly call this method. - """ - wrapped_act = self._wrapper(action) - return wrapped_act - - -def _continuous() -> Tuple[ - Callable[[Dict[str, np.ndarray]], Dict[str, np.ndarray]], gym.Space -]: - space = gym.spaces.Box( - low=np.array([0.0, 0.0, -1.0]), high=np.array([1.0, 1.0, 1.0]), dtype=np.float32 - ) - - def wrapper(action: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]: - return {k: v.astype(np.float32) for k, v in action.items()} - - return wrapper, space - - -def _lane() -> Tuple[Callable[[Dict[str, int]], Dict[str, str]], gym.Space]: - space = gym.spaces.Discrete(n=4) - - action_map = { - 0: "keep_lane", - 1: "slow_down", - 2: "change_lane_left", - 3: "change_lane_right", - } - - def wrapper(action: Dict[str, int]) -> Dict[str, str]: - return {k: action_map[v] for k, v in action.items()} - - return wrapper, space - - -def _actuator_dynamic() -> Tuple[ - Callable[[Dict[str, np.ndarray]], Dict[str, np.ndarray]], gym.Space -]: - space = gym.spaces.Box( - low=np.array([0.0, 0.0, -1.0]), high=np.array([1.0, 1.0, 1.0]), dtype=np.float32 - ) - - def wrapper(action: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]: - return {k: v.astype(np.float32) for k, v in action.items()} - - return wrapper, space - - -def _target_pose() -> Tuple[ - Callable[[Dict[str, np.ndarray]], Dict[str, np.ndarray]], gym.Space -]: - space = gym.spaces.Box( - low=np.array([-1e10, -1e10, -np.pi, 0]), - high=np.array([1e10, 1e10, np.pi, 1e10]), - dtype=np.float32, - ) - - def wrapper(action: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]: - return {k: v.astype(np.float32) for k, v in action.items()} - - return wrapper, space diff --git a/smarts/env/wrappers/format_obs.py b/smarts/env/wrappers/format_obs.py deleted file mode 100644 index dad8bb214a..0000000000 --- a/smarts/env/wrappers/format_obs.py +++ /dev/null @@ -1,656 +0,0 @@ -# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. - -import math -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -import gym -import numpy as np - -from smarts.core.events import Events -from smarts.core.observations import ( - DrivableAreaGridMap, - EgoVehicleObservation, - OccupancyGridMap, - SignalObservation, - TopDownRGB, - VehicleObservation, -) -from smarts.core.road_map import Waypoint - -_LIDAR_SHP = 300 -_NEIGHBOR_SHP = 10 -_WAYPOINT_SHP = (4, 20) -_SIGNALS_SHP = (3,) - -""" -Observations in numpy array format, suitable for vectorized processing. - -StdObs = dict({ - Total distance travelled in meters. - "distance_travelled": np.float32 - - Ego vehicle state, with the following attributes. - "ego_vehicle_state": dict({ - "angular_acceleration": - Angular acceleration vector. Requires `accelerometer` attribute - enabled in AgentInterface, else absent. shape=(3,). dtype=np.float32. - "angular_jerk": - Angular jerk vector. Requires `accelerometer` attribute enabled in - AgentInterface, else absent. shape=(3,). dtype=np.float32. - "angular_velocity": - Angular velocity vector. shape=(3,). dtype=np.float32). - "box": - Length, width, and height of the vehicle bounding box. shape=(3,). - dtype=np.float32. - "heading": - Vehicle heading in radians [-pi, pi]. dtype=np.float32. - "lane_index": - Vehicle's lane number. Rightmost lane has index 0 and increases - towards left. dtype=np.int8. - "linear_acceleration": - Vehicle acceleration in x, y, and z axes. Requires `accelerometer` - attribute enabled in AgentInterface, else absent. shape=(3,). - dtype=np.float32. - "linear_jerk": - Linear jerk vector. Requires `accelerometer` attribute enabled in - AgentInterface, else absent. shape=(3,). dtype=np.float32. - "linear_velocity": - Vehicle velocity in x, y, and z axes. shape=(3,). dtype=np.float32. - "pos": - Coordinate of the center of the vehicle bounding box's bottom plane. - shape=(3,). dtype=np.float64. - "speed": - Vehicle speed in m/s. dtype=np.float32. - "steering": - Angle of front wheels in radians [-pi, pi]. dtype=np.float32. - "yaw_rate": - Rotation speed around vertical axis in rad/s [0, 2pi]. - dtype=np.float32. - )} - - A dictionary of event markers. - "events": dict({ - "agents_alive_done": - 1 if `DoneCriteria.agents_alive` is triggered, else 0. - "collisions": - 1 if any collisions occurred with ego vehicle, else 0. - "not_moving": - 1 if `DoneCriteria.not_moving` is triggered, else 0. - "off_road": - 1 if ego vehicle drives off road, else 0. - "off_route": - 1 if ego vehicle drives off mission route, else 0. - "on_shoulder": - 1 if ego vehicle drives on road shoulder, else 0. - "reached_goal": - 1 if ego vehicle reaches its goal, else 0. - "reached_max_episode_steps": - 1 if maximum episode steps reached, else 0. - "wrong_way": - 1 if ego vehicle drives in the wrong traffic direction, else 0. - }) - - Drivable area grid map. Map is binary, with 255 if a cell contains a road, - else 0. dtype=np.uint8. - "drivable_area_grid_map": np.ndarray - - Lidar point cloud, with the following attributes. - "lidar_point_cloud": dict({ - "hit": - Binary array. 1 if an object is hit, else 0. shape(300,). - "point_cloud": - Coordinates of lidar point cloud. shape=(300,3). dtype=np.float64. - "ray_origin": - Ray origin coordinates. shape=(300,3). dtype=np.float64. - "ray_vector": - Ray vectors. shape=(300,3). dtype=np.float64. - }) - - Mission details for the ego agent. - "mission": dict({ - "goal_pos": - Achieve goal by reaching the end position. Defaults to np.array([0,0,0]) - for no mission. shape=(3,). dtype=np.float64. - }) - - Feature array of 10 nearest neighborhood vehicles. If nearest neighbor - vehicles are insufficient, default feature values are padded. - "neighborhood_vehicle_states": dict({ - "box": - Bounding box of neighbor vehicles. Defaults to np.array([0,0,0]) per - vehicle. shape=(10,3). dtype=np.float32. - "heading": - Heading of neighbor vehicles in radians [-pi, pi]. Defaults to - np.array([0]) per vehicle. shape=(10,). dtype=np.float32. - "lane_index": - Lane number of neighbor vehicles. Defaults to np.array([0]) per - vehicle. shape=(10,). dtype=np.int8. - "pos": - Coordinate of the center of neighbor vehicles' bounding box's bottom - plane. Defaults to np.array([0,0,0]) per vehicle. shape=(10,3). - dtype=np.float64. - "speed": - Speed of neighbor vehicles in m/s. Defaults to np.array([0]) per - vehicle. shape=(10,). dtype=np.float32. - }) - - Occupancy grid map. Map is binary, with 255 if a cell is occupied, else 0. - dtype=np.uint8. - "occupancy_grid_map": np.ndarray - - RGB image, from the top view, with ego vehicle at the center. - shape=(height, width, 3). dtype=np.uint8. - "top_down_rgb": np.ndarray - - Feature array of 20 waypoints ahead or in the mission route, from the - nearest 4 lanes. If lanes or waypoints ahead are insufficient, default - values are padded. - "waypoint_paths": dict({ - "heading": - Lane heading angle at a waypoint in radians [-pi, pi]. Defaults to - np.array([0]) per waypoint. shape=(4,20). dtype=np.float32. - "lane_index": - Lane number at a waypoint. Defaults to np.array([0]) per waypoint. - shape=(4,20). dtype=np.int8. - "lane_width": - Lane width at a waypoint in meters. Defaults to np.array([0]) per - waypoint. shape=(4,20). dtype=np.float32. - "pos": - Coordinate of a waypoint. Defaults to np.array([0,0,0]). - shape=(4,20,3). dtype=np.float64. - "speed_limit": - Lane speed limit at a waypoint in m/s. shape=(4,20). dtype=np.float32. - }) - - Feature array of 3 upcoming signals. If there aren't this many signals ahead, - default values are padded. - "signals": dict({ - "state": - The state of the traffic signal. - See smarts.core.signal_provider.SignalLightState for interpretation. - Defaults to np.array([0]) per signal. shape=(3,), dtype=np.int8. - "stop_point": - The stopping point for traffic controlled by the signal, i.e., the - point where actors should stop when the signal is in a stop state. - Defaults to np.array([0, 0]) per signal. shape=(3,2), dtype=np.float64. - "last_changed": - If known, the simulation time this signal last changed its state. - Defaults to np.array([0]) per signal. shape=(3,), dtype=np.float32. - }) -}) -""" - - -class FormatObs(gym.ObservationWrapper): - """Converts SMARTS observations to gym-compliant vectorized observations - and returns `StdObs`. The observation set returned depends on the features - enabled via AgentInterface. - - Note: - FormatObs wrapper requires all agents must have the same AgentInterface - attributes. - """ - - def __init__(self, env: gym.Env): - """ - Args: - env (gym.Env): SMARTS environment to be wrapped. - - Raises: - AssertionError: If all agents do not have the same AgentInterface. - """ - super().__init__(env) - - agent_id = next(iter(self.agent_ids)) - intrfcs = {} - for intrfc in { - "accelerometer", - "drivable_area_grid_map", - "lidar_point_cloud", - "neighborhood_vehicle_states", - "occupancy_grid_map", - "top_down_rgb", - "waypoint_paths", - "signals", - }: - val = getattr(self.agent_interfaces[agent_id], intrfc) - self._cmp_intrfc(intrfc, val) - if val: - intrfcs.update({intrfc: val}) - - self._space = _make_space(intrfcs) - self.observation_space = gym.spaces.Dict( - {agent_id: gym.spaces.Dict(self._space) for agent_id in self.agent_ids} - ) - - self._stdob_to_ob = { - "drivable_area_grid_map": "drivable_area_grid_map", - "distance_travelled": "distance_travelled", - "ego_vehicle_state": "ego_vehicle_state", - "events": "events", - "lidar_point_cloud": "lidar_point_cloud", - "mission": "ego_vehicle_state", - "neighborhood_vehicle_states": "neighborhood_vehicle_states", - "occupancy_grid_map": "occupancy_grid_map", - "top_down_rgb": "top_down_rgb", - "waypoint_paths": "waypoint_paths", - "signals": "signals", - } - self._accelerometer = "accelerometer" in intrfcs.keys() - - def _cmp_intrfc(self, intrfc: str, val: Any): - assert all( - getattr(self.agent_interfaces[agent_id], intrfc) == val - for agent_id in self.agent_ids - ), f"""To use FormatObs wrapper, all agents must have the same - AgentInterface.{intrfc} attribute.""" - - def observation(self, observation: Dict[str, Any]) -> Dict[str, Dict[str, Any]]: - """Converts SMARTS observations to gym-compliant vectorized - observations. - - Note: Users should not directly call this method. - """ - wrapped_obs = {} - for agent_id, agent_obs in observation.items(): - wrapped_ob = {} - for stdob in self._space.keys(): - func = globals()[f"_std_{stdob}"] - if stdob == "ego_vehicle_state": - val = func( - getattr(agent_obs, self._stdob_to_ob[stdob]), - self._accelerometer, - ) - else: - val = func(getattr(agent_obs, self._stdob_to_ob[stdob])) - wrapped_ob.update({stdob: val}) - wrapped_obs.update({agent_id: wrapped_ob}) - - return wrapped_obs - - -def intrfc_to_stdobs(intrfc: str) -> Optional[str]: - """Returns formatted observation name corresponding to the - AgentInterface attribute name. - - Args: - intrfc (str): AgentInterface attribute name. - - Returns: - Optional[str]: Corresponding formatted observation name. None, if - unavailable. - """ - return { - "drivable_area_grid_map": "drivable_area_grid_map", - "lidar_point_cloud": "lidar_point_cloud", - "neighborhood_vehicle_states": "neighborhood_vehicle_states", - "occupancy_grid_map": "occupancy_grid_map", - "top_down_rgb": "top_down_rgb", - "waypoint_paths": "waypoint_paths", - "signals": "signals", - }.get(intrfc, None) - - -def get_spaces() -> Dict[str, Callable[[Any], gym.Space]]: - """Returns the available gym spaces of a `StdObs`. - - Returns: - Dict[str, Callable[[Any], gym.Space]]: - Available gym spaces of a `StdObs`. - """ - # fmt: off - ego_basic = { - "angular_velocity": gym.spaces.Box(low=-1e10, high=1e10, shape=(3,), dtype=np.float32), - "box": gym.spaces.Box(low=0, high=1e10, shape=(3,), dtype=np.float32), - "heading": gym.spaces.Box(low=-math.pi, high=math.pi, shape=(), dtype=np.float32), - "lane_index": gym.spaces.Box(low=0, high=127, shape=(), dtype=np.int8), - "linear_velocity": gym.spaces.Box(low=-1e10, high=1e10, shape=(3,), dtype=np.float32), - "pos": gym.spaces.Box(low=-1e10, high=1e10, shape=(3,), dtype=np.float64), - "speed": gym.spaces.Box(low=0, high=1e10, shape=(), dtype=np.float32), - "steering": gym.spaces.Box(low=-math.pi, high=math.pi, shape=(), dtype=np.float32), - "yaw_rate": gym.spaces.Box(low=0, high=2*math.pi, shape=(), dtype=np.float32), - } - ego_opt = { - "angular_acceleration": gym.spaces.Box(low=-1e10, high=1e10, shape=(3,), dtype=np.float32), - "angular_jerk": gym.spaces.Box(low=-1e10, high=1e10, shape=(3,), dtype=np.float32), - "linear_acceleration": gym.spaces.Box(low=-1e10, high=1e10, shape=(3,), dtype=np.float32), - "linear_jerk": gym.spaces.Box(low=-1e10, high=1e10, shape=(3,), dtype=np.float32), - } - spaces = { - "distance_travelled": lambda _: gym.spaces.Box(low=0, high=1e10, shape=(), dtype=np.float32), - "ego_vehicle_state": lambda val: gym.spaces.Dict(dict(ego_basic, **ego_opt)) if val else gym.spaces.Dict(ego_basic), - "events": lambda _: gym.spaces.Dict({ - "agents_alive_done": gym.spaces.Discrete(n=2), - "collisions": gym.spaces.Discrete(n=2), - "not_moving": gym.spaces.Discrete(n=2), - "off_road": gym.spaces.Discrete(n=2), - "off_route": gym.spaces.Discrete(n=2), - "on_shoulder": gym.spaces.Discrete(n=2), - "reached_goal": gym.spaces.Discrete(n=2), - "reached_max_episode_steps": gym.spaces.Discrete(n=2), - "wrong_way": gym.spaces.Discrete(n=2), - }), - "drivable_area_grid_map": lambda val: gym.spaces.Box(low=0, high=255, shape=(val.height, val.width, 1), dtype=np.uint8), - "lidar_point_cloud": lambda _: gym.spaces.Dict({ - "hit": gym.spaces.MultiBinary(_LIDAR_SHP), - "point_cloud": gym.spaces.Box(low=-1e10, high=1e10, shape=(_LIDAR_SHP,3), dtype=np.float64), - "ray_origin": gym.spaces.Box(low=-1e10, high=1e10, shape=(_LIDAR_SHP,3), dtype=np.float64), - "ray_vector": gym.spaces.Box(low=-1e10, high=1e10, shape=(_LIDAR_SHP,3), dtype=np.float64), - }), - "mission": lambda _: gym.spaces.Dict({ - "goal_pos": gym.spaces.Box(low=-1e10, high=1e10, shape=(3,), dtype=np.float64), - }), - "neighborhood_vehicle_states": lambda _: gym.spaces.Dict({ - "box": gym.spaces.Box(low=0, high=1e10, shape=(_NEIGHBOR_SHP,3), dtype=np.float32), - "heading": gym.spaces.Box(low=-math.pi, high=math.pi, shape=(_NEIGHBOR_SHP,), dtype=np.float32), - "lane_index": gym.spaces.Box(low=0, high=127, shape=(_NEIGHBOR_SHP,), dtype=np.int8), - "pos": gym.spaces.Box(low=-1e10, high=1e10, shape=(_NEIGHBOR_SHP,3), dtype=np.float64), - "speed": gym.spaces.Box(low=0, high=1e10, shape=(_NEIGHBOR_SHP,), dtype=np.float32), - "interest": gym.spaces.MultiBinary(_NEIGHBOR_SHP), - }), - "occupancy_grid_map": lambda val: gym.spaces.Box(low=0, high=255,shape=(val.height, val.width, 1), dtype=np.uint8), - "top_down_rgb": lambda val: gym.spaces.Box(low=0, high=255, shape=(val.height, val.width, 3), dtype=np.uint8), - "waypoint_paths": lambda _: gym.spaces.Dict({ - "heading": gym.spaces.Box(low=-math.pi, high=math.pi, shape=_WAYPOINT_SHP, dtype=np.float32), - "lane_index": gym.spaces.Box(low=0, high=127, shape=_WAYPOINT_SHP, dtype=np.int8), - "lane_width": gym.spaces.Box(low=0, high=1e10, shape=_WAYPOINT_SHP, dtype=np.float32), - "pos": gym.spaces.Box(low=-1e10, high=1e10, shape=_WAYPOINT_SHP + (3,), dtype=np.float64), - "speed_limit": gym.spaces.Box(low=0, high=1e10, shape=_WAYPOINT_SHP, dtype=np.float32), - }), - "signals": lambda _: gym.spaces.Dict({ - "state": gym.spaces.Box(low=0, high=32, shape=_SIGNALS_SHP, dtype=np.int8), - "stop_point": gym.spaces.Box(low=-1e10, high=1e10, shape=_SIGNALS_SHP + (2,), dtype=np.float64), - "last_changed": gym.spaces.Box(low=0, high=1e10, shape=_SIGNALS_SHP, dtype=np.float32), - }), - } - # fmt: on - - return spaces - - -def _make_space(intrfcs: Dict[str, Any]) -> Dict[str, gym.Space]: - spaces = get_spaces() - space = {} - - space.update( - { - "distance_travelled": spaces["distance_travelled"](None), - "ego_vehicle_state": spaces["ego_vehicle_state"]( - "accelerometer" in intrfcs.keys() - ), - "events": spaces["events"](None), - "mission": spaces["mission"](None), - } - ) - - for intrfc, val in intrfcs.items(): - opt_ob = intrfc_to_stdobs(intrfc) - if opt_ob: - space.update({opt_ob: spaces[opt_ob](val)}) - - return space - - -def _std_drivable_area_grid_map( - val: DrivableAreaGridMap, -) -> np.ndarray: - return val.data.astype(np.uint8) - - -def _std_distance_travelled(val: float) -> float: - return np.float32(val) - - -def _std_ego_vehicle_state( - val: EgoVehicleObservation, accelerometer: bool -) -> Dict[str, Union[np.int8, np.float32, np.ndarray]]: - - std_ego = { - "angular_velocity": val.angular_velocity.astype(np.float32), - "box": np.array(val.bounding_box.as_lwh).astype(np.float32), - "heading": np.float32(val.heading), - "lane_index": np.int8(val.lane_index), - "linear_velocity": val.linear_velocity.astype(np.float32), - "pos": val.position.astype(np.float64), - "speed": np.float32(val.speed), - "steering": np.float32(val.steering), - "yaw_rate": np.float32(val.yaw_rate) if val.yaw_rate else np.float32(0), - } - - if accelerometer: - std_ego.update( - { - "angular_acceleration": val.angular_acceleration.astype(np.float32), - "angular_jerk": val.angular_jerk.astype(np.float32), - "linear_acceleration": val.linear_acceleration.astype(np.float32), - "linear_jerk": val.linear_jerk.astype(np.float32), - } - ) - - return std_ego - - -def _std_events(val: Events) -> Dict[str, int]: - return { - "agents_alive_done": np.int64(val.agents_alive_done), - "collisions": np.int64(len(val.collisions) > 0), - "not_moving": np.int64(val.not_moving), - "off_road": np.int64(val.off_road), - "off_route": np.int64(val.off_route), - "on_shoulder": np.int64(val.on_shoulder), - "reached_goal": np.int64(val.reached_goal), - "reached_max_episode_steps": np.int64(val.reached_max_episode_steps), - "wrong_way": np.int64(val.wrong_way), - } - - -def _std_lidar_point_cloud( - val: Tuple[List[np.ndarray], List[np.ndarray], List[Tuple[np.ndarray, np.ndarray]]] -) -> Dict[str, np.ndarray]: - - des_shp = _LIDAR_SHP - hit = np.array(val[1], dtype=np.int8) - point_cloud = np.array(val[0], dtype=np.float64) - point_cloud = np.nan_to_num( - point_cloud, - copy=False, - nan=np.float64(0), - posinf=np.float64(0), - neginf=np.float64(0), - ) - ray_origin, ray_vector = zip(*(val[2])) - ray_origin = np.array(ray_origin, np.float64) - ray_vector = np.array(ray_vector, np.float64) - - try: - assert hit.shape == (des_shp,) - assert point_cloud.shape == (des_shp, 3) - assert ray_origin.shape == (des_shp, 3) - assert ray_vector.shape == (des_shp, 3) - except: - raise Exception("Internal Error: Mismatched lidar point cloud shape.") - - return { - "hit": hit, - "point_cloud": point_cloud, - "ray_origin": ray_origin, - "ray_vector": ray_vector, - } - - -def _std_mission(val: EgoVehicleObservation) -> Dict[str, np.ndarray]: - - if hasattr(val.mission.goal, "position"): - goal_pos = np.array(val.mission.goal.position, dtype=np.float64) - else: - goal_pos = np.zeros((3,), dtype=np.float64) - - return {"goal_pos": goal_pos} - - -def _std_neighborhood_vehicle_states( - nghbs: List[VehicleObservation], -) -> Dict[str, np.ndarray]: - - des_shp = _NEIGHBOR_SHP - rcv_shp = len(nghbs) - pad_shp = 0 if des_shp - rcv_shp < 0 else des_shp - rcv_shp - - if rcv_shp == 0: - return { - "box": np.zeros((des_shp, 3), dtype=np.float32), - "heading": np.zeros((des_shp,), dtype=np.float32), - "lane_index": np.zeros((des_shp,), dtype=np.int8), - "pos": np.zeros((des_shp, 3), dtype=np.float64), - "speed": np.zeros((des_shp,), dtype=np.float32), - "interest": np.zeros((des_shp,), dtype=np.int8), - } - - nghbs = [ - ( - nghb.bounding_box.as_lwh, - nghb.heading, - nghb.lane_index, - nghb.position, - nghb.speed, - nghb.interest, - ) - for nghb in nghbs[:des_shp] - ] - box, heading, lane_index, pos, speed, interest = zip(*nghbs) - - box = np.array(box, dtype=np.float32) - heading = np.array(heading, dtype=np.float32) - lane_index = np.array(lane_index, dtype=np.int8) - pos = np.array(pos, dtype=np.float64) - speed = np.array(speed, dtype=np.float32) - interest = np.array(interest, dtype=np.int8) - - # fmt: off - box = np.pad(box, ((0,pad_shp),(0,0)), mode='constant', constant_values=0) - heading = np.pad(heading, ((0,pad_shp)), mode='constant', constant_values=0) - lane_index = np.pad(lane_index, ((0,pad_shp)), mode='constant', constant_values=0) - pos = np.pad(pos, ((0,pad_shp),(0,0)), mode='constant', constant_values=0) - speed = np.pad(speed, ((0,pad_shp)), mode='constant', constant_values=0) - interest = np.pad(interest, ((0,pad_shp)), mode="constant", constant_values=0) - # fmt: on - - return { - "box": box, - "heading": heading, - "lane_index": lane_index, - "pos": pos, - "speed": speed, - "interest": interest, - } - - -def _std_occupancy_grid_map(val: OccupancyGridMap) -> np.ndarray: - return val.data.astype(np.uint8) - - -def _std_top_down_rgb(val: TopDownRGB) -> np.ndarray: - return val.data.astype(np.uint8) - - -def _std_waypoint_paths( - rcv_paths: List[List[Waypoint]], -) -> Dict[str, np.ndarray]: - - # Truncate all paths to be of the same length - min_len = min(map(len, rcv_paths)) - trunc_paths = list(map(lambda x: x[:min_len], rcv_paths)) - - des_shp = _WAYPOINT_SHP - rcv_shp = (len(trunc_paths), len(trunc_paths[0])) - pad_shp = [0 if des - rcv < 0 else des - rcv for des, rcv in zip(des_shp, rcv_shp)] - - def extract_elem(waypoint): - return ( - waypoint.heading, - waypoint.lane_index, - waypoint.lane_width, - waypoint.pos, - waypoint.speed_limit, - ) - - paths = [ - map(extract_elem, path[: des_shp[1]]) for path in trunc_paths[: des_shp[0]] - ] - heading, lane_index, lane_width, pos, speed_limit = zip( - *[zip(*path) for path in paths] - ) - - heading = np.array(heading, dtype=np.float32) - lane_index = np.array(lane_index, dtype=np.int8) - lane_width = np.array(lane_width, dtype=np.float32) - pos = np.array(pos, dtype=np.float64) - speed_limit = np.array(speed_limit, dtype=np.float32) - - # fmt: off - heading = np.pad(heading, ((0,pad_shp[0]),(0,pad_shp[1])), mode='constant', constant_values=0) - lane_index = np.pad(lane_index, ((0,pad_shp[0]),(0,pad_shp[1])), mode='constant', constant_values=0) - lane_width = np.pad(lane_width, ((0,pad_shp[0]),(0,pad_shp[1])), mode='constant', constant_values=0) - pos = np.pad(pos, ((0,pad_shp[0]),(0,pad_shp[1]),(0,1)), mode='constant', constant_values=0) - speed_limit = np.pad(speed_limit, ((0,pad_shp[0]),(0,pad_shp[1])), mode='constant', constant_values=0) - # fmt: on - - return { - "heading": heading, - "lane_index": lane_index, - "lane_width": lane_width, - "pos": pos, - "speed_limit": speed_limit, - } - - -def _std_signals( - signals: List[SignalObservation], -) -> Dict[str, np.ndarray]: - - des_shp = _SIGNALS_SHP - rcv_shp = len(signals) - pad_shp = max(0, des_shp[0] - rcv_shp) - - if rcv_shp == 0: - return { - "state": np.zeros(des_shp, dtype=np.int8), - "stop_point": np.zeros(des_shp + (2,), dtype=np.float64), - "last_changed": np.zeros(des_shp, dtype=np.float32), - } - - signals = [ - (signal.state, signal.stop_point, signal.last_changed) - for signal in signals[: des_shp[0]] - ] - state, stop_point, last_changed = zip(*signals) - - # fmt: off - state = np.pad(state, ((0, pad_shp)), mode='constant', constant_values=0) - stop_point = np.pad(state, ((0, pad_shp), (0, 0)), mode='constant', constant_values=0) - last_changed = np.pad(last_changed, ((0, pad_shp)), mode='constant', constant_values=0) - # fmt: on - - return { - "state": state, - "stop_point": stop_point, - "last_changed": last_changed, - }