feature(zc): add carracing in box2d

dizoo/box2d/carracing/config/__init__.py

@@ -0,0 +1 @@
+from .carracing_dqn_config import carracing_dqn_config, carracing_dqn_create_config

dizoo/box2d/carracing/config/carracing_dqn_config.py

@@ -0,0 +1,81 @@
+from easydict import EasyDict
+
+nstep = 3
+carracing_dqn_config = dict(
+    exp_name='carracing_dqn_seed0',
+    env=dict(
+        # Whether to use shared memory. Only effective if "env_manager_type" is 'subprocess'
+        # Env number respectively for collector and evaluator.
+        collector_env_num=8,
+        evaluator_env_num=8,
+        env_id='CarRacing-v2',
+        continuous=False,
+        n_evaluator_episode=8,
+        stop_value=950,
+        # The path to save the game replay
+        # replay_path='./carracing_dqn_seed0/video',
+    ),
+    policy=dict(
+        # Whether to use cuda for network.
+        cuda=True,
+        load_path="./carracing_seed0/ckpt/ckpt_best.pth.tar",
+        model=dict(
+            obs_shape=[3, 96, 96],
+            action_shape=5,
+            encoder_hidden_size_list=[128, 128, 512],
+            # Whether to use dueling head.
+            dueling=True,
+        ),
+        # Reward's future discount factor, aka. gamma.
+        discount_factor=0.99,
+        # How many steps in td error.
+        nstep=nstep,
+        # learn_mode config
+        learn=dict(
+            update_per_collect=10,
+            batch_size=64,
+            learning_rate=0.001,
+            # Frequency of target network update.
+            target_update_freq=100,
+        ),
+        # collect_mode config
+        collect=dict(
+            # You can use either "n_sample" or "n_episode" in collector.collect.
+            # Get "n_sample" samples per collect.
+            n_sample=64,
+            # Cut trajectories into pieces with length "unroll_len".
+            unroll_len=1,
+        ),
+        # command_mode config
+        other=dict(
+            # Epsilon greedy with decay.
+            eps=dict(
+                # Decay type. Support ['exp', 'linear'].
+                type='exp',
+                start=0.95,
+                end=0.1,
+                decay=50000,
+            ),
+            replay_buffer=dict(replay_buffer_size=100000, )
+        ),
+    ),
+)
+carracing_dqn_config = EasyDict(carracing_dqn_config)
+main_config = carracing_dqn_config
+
+carracing_dqn_create_config = dict(
+    env=dict(
+        type='carracing',
+        import_names=['dizoo.box2d.carracing.envs.carracing_env'],
+    ),
+    env_manager=dict(type='subprocess'),
+    # env_manager=dict(type='base'),
+    policy=dict(type='dqn'),
+)
+carracing_dqn_create_config = EasyDict(carracing_dqn_create_config)
+create_config = carracing_dqn_create_config
+
+if __name__ == "__main__":
+    # or you can enter `ding -m serial -c lunarlander_dqn_config.py -s 0`
+    from ding.entry import serial_pipeline
+    serial_pipeline([main_config, create_config], seed=0)

dizoo/box2d/carracing/envs/__init__.py

@@ -0,0 +1 @@
+from .carracing_env import CarRacingEnv

dizoo/box2d/carracing/envs/carracing_env.py

@@ -0,0 +1,161 @@
+import copy
+import os
+from typing import Optional
+
+import gym
+import numpy as np
+from easydict import EasyDict
+
+from ding.envs import BaseEnv, BaseEnvTimestep
+from ding.envs import ObsPlusPrevActRewWrapper
+from ding.envs.common import affine_transform, save_frames_as_gif
+from ding.torch_utils import to_ndarray
+from ding.utils import ENV_REGISTRY
+import cv2
+
+@ENV_REGISTRY.register('carracing')
+class CarRacingEnv(BaseEnv):
+
+    config = dict(
+        replay_path=None,
+        save_replay_gif=False,
+        replay_path_gif=None,
+        action_clip=False,
+    )
+
+    @classmethod
+    def default_config(cls:type) -> EasyDict:
+        cfg = EasyDict(copy.deepcopy(cls.config))
+        cfg.cfg_type = cls.__name__ + 'Dict'
+        return cfg
+
+    def __init__(self, cfg: dict) -> None:
+        self._cfg = cfg
+        self._init_flag = False
+        # env_id:CarRacing-v2
+        self._env_id = cfg.env_id
+        self._replay_path = None
+        self._replay_path_gif = cfg.replay_path_gif
+        self._save_replay_gif = cfg.save_replay_gif
+        self._save_replay_count = 0
+        if 'Continuous' in self._env_id:
+            self._act_scale = cfg.act_scale  # act_scale only works in continuous env
+            self._action_clip = cfg.action_clip
+        else:
+            self._act_scale = False
+
+    def reset(self) -> np.ndarray:
+        if not self._init_flag:
+            self._env = gym.make(self._cfg.env_id,continuous=self._cfg.continuous)
+            if self._replay_path is not None:
+                self._env = gym.wrappers.RecordVideo(
+                    self._env,
+                    video_folder=self._replay_path,
+                    episode_trigger=lambda episode_id: True,
+                    name_prefix='rl-video-{}'.format(id(self))
+                )
+            if hasattr(self._cfg, 'obs_plus_prev_action_reward') and self._cfg.obs_plus_prev_action_reward:
+                self._env = ObsPlusPrevActRewWrapper(self._env)
+            # self._observation_space = self._env.observation_space
+            self._observation_space = gym.spaces.Box(
+                low = np.min(self._env.observation_space.low), high = np.max(self._env.observation_space.high),
+                shape = (self._env.observation_space.shape[2], 
+                self._env.observation_space.shape[0], self._env.observation_space.shape[1]),
+                dtype = np.float32
+            )
+            self._action_space = self._env.action_space
+            self._reward_space = gym.spaces.Box(
+                low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32
+            )
+            self._init_flag = True
+        if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed:
+            np_seed = 100 * np.random.randint(1, 1000)
+            self._env.seed(self._seed + np_seed)
+        elif hasattr(self, '_seed'):
+            self._env.seed(self._seed)
+        self._eval_episode_return = 0
+        obs = self._env.reset()
+        obs = obs.astype(np.float32) / 255
+        obs = obs.transpose(2, 0, 1)
+        obs = to_ndarray(obs)
+        if self._save_replay_gif:
+            self._frames = []
+        return obs
+
+    def close(self) -> None:
+        if self._init_flag:
+            self._env.close()
+        self._init_flag = False
+
+    def render(self) -> None:
+        self._env.render()
+
+    def seed(self, seed: int, dynamic_seed: bool = True) -> None:
+        self._seed = seed
+        self._dynamic_seed = dynamic_seed
+        np.random.seed(self._seed)
+
+    def step(self, action: np.ndarray) -> BaseEnvTimestep:
+        assert isinstance(action, np.ndarray), type(action)
+        if action.shape == (1, ):
+            action = action.item()  # 0-dim array
+        if self._act_scale:
+            action = affine_transform(action, action_clip=self._action_clip, min_val=-1, max_val=1)
+        if self._save_replay_gif:
+            self._frames.append(self._env.render(mode='rgb_array'))
+        obs, rew, done, info = self._env.step(action)
+        obs = obs.astype(np.float32) / 255
+        obs = obs.transpose(2, 0, 1)
+        self._eval_episode_return += rew
+        if done:
+            info['eval_episode_return'] = self._eval_episode_return
+            if self._save_replay_gif:
+                if not os.path.exists(self._replay_path_gif):
+                    os.makedirs(self._replay_path_gif)
+                path = os.path.join(
+                    self._replay_path_gif, '{}_episode_{}.gif'.format(self._env_id, self._save_replay_count)
+                )
+                save_frames_as_gif(self._frames, path)
+                self._save_replay_count += 1
+
+        obs = to_ndarray(obs)
+        rew = to_ndarray([rew]).astype(np.float32)  # wrapped to be transferred to a array with shape (1,)
+        return BaseEnvTimestep(obs, rew, done, info)
+
+
+    def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
+        if replay_path is None:
+            replay_path = './video'
+        self._replay_path = replay_path
+        self._save_replay_gif = True
+        self._save_replay_count = 0
+        # this function can lead to the meaningless result
+        self._env = gym.wrappers.RecordVideo(
+            self._env,
+            video_folder=self._replay_path,
+            episode_trigger=lambda episode_id: True,
+            name_prefix='rl-video-{}'.format(id(self))
+        )
+
+    def random_action(self) -> np.ndarray:
+        random_action = self.action_space.sample()
+        if isinstance(random_action, np.ndarray):
+            pass
+        elif isinstance(random_action, int):
+            random_action = to_ndarray([random_action], dtype=np.int64)
+        return random_action
+
+    @property
+    def observation_space(self) -> gym.spaces.Space:
+        return self._observation_space
+
+    @property
+    def action_space(self) -> gym.spaces.Space:
+        return self._action_space
+
+    @property
+    def reward_space(self) -> gym.spaces.Space:
+        return self._reward_space
+
+    def __repr__(self) -> str:
+        return "DI-engine LunarLander Env"

dizoo/box2d/carracing/envs/test_carracing_env.py

@@ -0,0 +1,40 @@
+from time import time
+import pytest
+import numpy as np
+from easydict import EasyDict
+from carracing_env import CarRacingEnv
+
+@pytest.mark.envtest
+@pytest.mark.parametrize(
+    'cfg', [
+        EasyDict({
+            'env_id': 'CarRacing-v1',
+            'act_scale': False
+        }),
+        EasyDict({
+            'env_id': 'CarRacingContinuous-v1',
+            'act_scale': True
+        })
+    ]
+)
+
+class TestCarRacing:
+     def test_naive(self, cfg):
+        env = CarRacingEnv(cfg)
+        env.seed(314)
+        assert env._seed == 314
+        obs = env.reset()
+        assert obs.shape == (96, 96, 3)
+        for i in range(10):
+            random_action = env.random_action()
+            timestep = env.step(random_action)
+            print(timestep)
+            assert isinstance(timestep.obs, np.ndarray)
+            assert isinstance(timestep.done, bool)
+            assert timestep.obs.shape == (96, 96, 3)
+            assert timestep.reward.shape == (1, )
+            assert timestep.reward >= env.reward_space.low
+            assert timestep.reward <= env.reward_space.high
+            # assert isinstance(timestep, tuple)
+        print(env.observation_space, env.action_space, env.reward_space)
+        env.close()