feature(whl): add more DingEnvWrapper example

dizoo/dmc2gym/config/dmc2gym_ppo_config.py

@@ -0,0 +1,64 @@
+from easydict import EasyDict
+
+
+cartpole_balance_ppo_config = dict(
+    exp_name='dmc2gym_cartpole_balance_ppo',
+    env=dict(
+        env_id='dmc2gym_cartpole_balance',
+        domain_name='cartpole',
+        task_name='balance',
+        from_pixels=False,
+        norm_obs=dict(use_norm=False, ),
+        norm_reward=dict(use_norm=False, ),
+        collector_env_num=1,
+        evaluator_env_num=8,
+        use_act_scale=True,
+        n_evaluator_episode=8,
+        stop_value=1000,
+    ),
+    policy=dict(
+        cuda=True,
+        recompute_adv=True,
+        action_space='discrete',
+        model=dict(
+            obs_shape=5,
+            action_shape=1,
+            action_space='discrete',
+            encoder_hidden_size_list=[64, 64, 128],
+            critic_head_hidden_size=128,
+            actor_head_hidden_size=128,
+        ),
+        learn=dict(
+            epoch_per_collect=2,
+            batch_size=64,
+            learning_rate=0.001,
+            value_weight=0.5,
+            entropy_weight=0.01,
+            clip_ratio=0.2,
+            learner=dict(hook=dict(save_ckpt_after_iter=100)),
+        ),
+        collect=dict(
+            n_sample=256,
+            unroll_len=1,
+            discount_factor=0.9,
+            gae_lambda=0.95,
+        ),
+        other=dict(replay_buffer=dict(replay_buffer_size=10000, ), ),
+    )
+)
+cartpole_balance_ppo_config = EasyDict(cartpole_balance_ppo_config)
+main_config = cartpole_balance_ppo_config
+
+cartpole_balance_create_config = dict(
+    env=dict(
+        type='dmc2gym',
+        import_names=['dizoo.dmc2gym.envs.dmc2gym_env'],
+    ),
+    env_manager=dict(type='base'),
+    policy=dict(type='ppo'),
+    replay_buffer=dict(type='naive', ),
+)
+cartpole_balance_create_config = EasyDict(cartpole_balance_create_config)
+create_config = cartpole_balance_create_config
+
+# To use this config, you can enter dizoo/dmc2gym/entry to call dmc2gym_onppo_main.py

dizoo/dmc2gym/entry/dmc2gym_onppo_main.py

@@ -0,0 +1,122 @@
+import os
+from easydict import EasyDict
+from functools import partial
+from tensorboardX import SummaryWriter
+import dmc2gym
+
+from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator
+from ding.model import VAC
+from ding.policy import PPOPolicy
+from ding.envs import DingEnvWrapper, FinalEvalRewardEnv, BaseEnvManager
+from ding.config import compile_config
+from ding.utils import set_pkg_seed
+from dizoo.dmc2gym.config.dmc2gym_ppo_config import cartpole_balance_ppo_config
+from dizoo.dmc2gym.envs.dmc2gym_env import *
+
+
+class Dmc2GymWrapper(gym.Wrapper):
+    def __init__(self, env, cfg):
+        super().__init__(env)
+        cfg = EasyDict(cfg)
+        self._cfg = cfg
+
+        env_info = dmc2gym_env_info[cfg.domain_name][cfg.task_name]
+
+        self._observation_space = env_info["observation_space"](
+            from_pixels=self._cfg["from_pixels"],
+            height=self._cfg["height"],
+            width=self._cfg["width"],
+            channels_first=self._cfg["channels_first"]
+        )
+        self._action_space = env_info["action_space"]
+        self._reward_space = env_info["reward_space"](self._cfg["frame_skip"])
+
+    def _process_obs(self, obs):
+        if self._cfg["from_pixels"]:
+            obs = to_ndarray(obs).astype(np.uint8)
+        else:
+            obs = to_ndarray(obs).astype(np.float32)
+        return obs
+
+    def step(self, action):
+        action = np.array([action]).astype('float32')
+        obs, reward, done, info = self.env.step(action)
+        return self._process_obs(obs), reward, done, info
+
+    def reset(self):
+        obs = self.env.reset()
+        return self._process_obs(obs)
+
+
+def wrapped_dmc2gym_env(cfg):
+    default_cfg = {
+            "frame_skip": 3,
+            "from_pixels": True,
+            "visualize_reward": False,
+            "height": 100,
+            "width": 100,
+            "channels_first": True,
+    }
+    default_cfg.update(cfg)
+
+
+    return DingEnvWrapper(
+        dmc2gym.make(
+            domain_name=default_cfg["domain_name"],
+            task_name=default_cfg["task_name"],
+            seed=1,
+            visualize_reward=default_cfg["visualize_reward"],
+            from_pixels=default_cfg["from_pixels"],
+            height=default_cfg["height"],
+            width=default_cfg["width"],
+            frame_skip=default_cfg["frame_skip"]
+        )
+        ,
+        cfg={
+            'env_wrapper': [
+                lambda env: Dmc2GymWrapper(env, default_cfg),
+                lambda env: FinalEvalRewardEnv(env),
+            ]
+        }
+    )
+
+
+def main(cfg, seed=0, max_env_step=int(1e10), max_train_iter=int(1e10)):
+    cfg = compile_config(
+        cfg, BaseEnvManager, PPOPolicy, BaseLearner, SampleSerialCollector, InteractionSerialEvaluator,
+        save_cfg=True
+    )
+    collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
+    collector_env = BaseEnvManager(env_fn=[partial(wrapped_dmc2gym_env, cfg=cartpole_balance_ppo_config.env)
+                                           for _ in range(collector_env_num)], cfg=cfg.env.manager)
+    evaluator_env = BaseEnvManager(env_fn=[partial(wrapped_dmc2gym_env, cfg=cartpole_balance_ppo_config.env)
+                                           for _ in range(evaluator_env_num)], cfg=cfg.env.manager)
+
+    collector_env.seed(seed)
+    evaluator_env.seed(seed, dynamic_seed=False)
+    set_pkg_seed(seed, use_cuda=cfg.policy.cuda)
+
+    model = VAC(**cfg.policy.model)
+    policy = PPOPolicy(cfg.policy, model=model)
+    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
+    learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
+    collector = SampleSerialCollector(
+        cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name
+    )
+    evaluator = InteractionSerialEvaluator(
+        cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
+    )
+
+    while True:
+        if evaluator.should_eval(learner.train_iter):
+            stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
+            if stop:
+                break
+        new_data = collector.collect(train_iter=learner.train_iter)
+        learner.train(new_data, collector.envstep)
+        if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
+            break
+
+
+if __name__ == '__main__':
+    main(cartpole_balance_ppo_config)

dizoo/minigrid/entry/minigrid_onppo_main.py

@@ -0,0 +1,94 @@
+import gym
+from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator
+from ding.model import VAC
+from ding.policy import PPOPolicy
+from ding.envs import DingEnvWrapper, FinalEvalRewardEnv, BaseEnvManager
+from ding.config import compile_config
+from ding.utils import set_pkg_seed
+
+from dizoo.minigrid.config.minigrid_onppo_config import minigrid_ppo_config
+from minigrid.wrappers import FlatObsWrapper
+import numpy as np
+from tensorboardX import SummaryWriter
+import os
+import gymnasium
+
+
+class MinigridWrapper(gym.Wrapper):
+    def __init__(self, env):
+        super().__init__(env)
+        self._observation_space = gym.spaces.Box(
+            low=float("-inf"),
+            high=float("inf"),
+            shape=(8,),
+            dtype=np.float32
+        )
+        self._action_space = gym.spaces.Discrete(9)
+        self._action_space.seed(0)  # default seed
+        self.reward_range = (float('-inf'), float('inf'))
+        self.max_steps = minigrid_ppo_config.env.max_step
+
+    def step(self, action):
+        obs, reward, done, _, info = self.env.step(action)
+        self.cur_step += 1
+        if self.cur_step > self.max_steps:
+            done = True
+        return obs, reward, done, info
+
+    def reset(self):
+        self.cur_step = 0
+        return self.env.reset()[0]
+
+
+def wrapped_minigrid_env():
+    return DingEnvWrapper(
+        gymnasium.make(minigrid_ppo_config.env.env_id),
+        cfg={
+            'env_wrapper': [
+                lambda env: FlatObsWrapper(env),
+                lambda env: MinigridWrapper(env),
+                lambda env: FinalEvalRewardEnv(env),
+            ]
+        }
+    )
+
+
+def main(cfg, seed=0,  max_env_step=int(1e10), max_train_iter=int(1e10)):
+    cfg = compile_config(
+        cfg, BaseEnvManager, PPOPolicy, BaseLearner, SampleSerialCollector, InteractionSerialEvaluator,
+        save_cfg=True
+    )
+    collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
+    collector_env = BaseEnvManager(env_fn=[wrapped_minigrid_env for _ in range(collector_env_num)],
+                                   cfg=cfg.env.manager)
+    evaluator_env = BaseEnvManager(env_fn=[wrapped_minigrid_env for _ in range(evaluator_env_num)],
+                                   cfg=cfg.env.manager)
+
+    collector_env.seed(seed)
+    evaluator_env.seed(seed, dynamic_seed=False)
+    set_pkg_seed(seed, use_cuda=cfg.policy.cuda)
+
+    model = VAC(**cfg.policy.model)
+    policy = PPOPolicy(cfg.policy, model=model)
+    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
+    learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
+    collector = SampleSerialCollector(
+        cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name
+    )
+    evaluator = InteractionSerialEvaluator(
+        cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
+    )
+
+    while True:
+        if evaluator.should_eval(learner.train_iter):
+            stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
+            if stop:
+                break
+        new_data = collector.collect(train_iter=learner.train_iter)
+        learner.train(new_data, collector.envstep)
+        if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
+            break
+
+
+if __name__ == '__main__':
+    main(minigrid_ppo_config)

dizoo/procgen/entry/bigfish_plr_config.py → dizoo/procgen/config/bigfish_plr_config.py

@@ -1,6 +1,6 @@
 from easydict import EasyDict
 
-bigfish_plr_default_config = dict(
+bigfish_plr_config = dict(
     exp_name='bigfish_plr_seed1',
     env=dict(
         is_train=True,
@@ -42,8 +42,8 @@
         temperature=0.1,
     ),
 )
-bigfish_plr_default_config = EasyDict(bigfish_plr_default_config)
-main_config = bigfish_plr_default_config
+bigfish_plr_config = EasyDict(bigfish_plr_config)
+main_config = bigfish_plr_config
 
 bigfish_plr_create_config = dict(
     env=dict(

dizoo/procgen/entry/bigfish_ppg_config.py → dizoo/procgen/config/bigfish_ppg_config.py

@@ -1,6 +1,6 @@
 from easydict import EasyDict
 
-bigfish_ppg_default_config = dict(
+bigfish_ppg_config = dict(
     exp_name='bigfish_ppg_seed0',
     env=dict(
         is_train=True,
@@ -37,8 +37,8 @@
         other=dict(),
     ),
 )
-bigfish_ppg_default_config = EasyDict(bigfish_ppg_default_config)
-main_config = bigfish_ppg_default_config
+bigfish_ppg_config = EasyDict(bigfish_ppg_config)
+main_config = bigfish_ppg_config
 
 bigfish_ppg_create_config = dict(
     env=dict(

dizoo/procgen/entry/coinrun_dqn_config.py → dizoo/procgen/config/coinrun_dqn_config.py

@@ -1,6 +1,6 @@
 from easydict import EasyDict
 
-coinrun_dqn_default_config = dict(
+coinrun_dqn_config = dict(
     env=dict(
         env_id='coinrun',
         collector_env_num=4,
@@ -36,8 +36,8 @@
         ),
     ),
 )
-coinrun_dqn_default_config = EasyDict(coinrun_dqn_default_config)
-main_config = coinrun_dqn_default_config
+coinrun_dqn_config = EasyDict(coinrun_dqn_config)
+main_config = coinrun_dqn_config
 
 coinrun_dqn_create_config = dict(
     env=dict(

dizoo/procgen/entry/coinrun_ppg_config.py → dizoo/procgen/config/coinrun_ppg_config.py

@@ -1,6 +1,6 @@
 from easydict import EasyDict
 
-coinrun_ppg_default_config = dict(
+coinrun_ppg_config = dict(
     exp_name='coinrun_ppg_seed0',
     env=dict(
         is_train=True,
@@ -37,8 +37,8 @@
         other=dict(),
     ),
 )
-coinrun_ppg_default_config = EasyDict(coinrun_ppg_default_config)
-main_config = coinrun_ppg_default_config
+coinrun_ppg_config = EasyDict(coinrun_ppg_config)
+main_config = coinrun_ppg_config
 
 coinrun_ppg_create_config = dict(
     env=dict(

dizoo/procgen/entry/coinrun_ppo_config.py → dizoo/procgen/config/coinrun_ppo_config.py

@@ -1,6 +1,6 @@
 from easydict import EasyDict
 
-coinrun_ppo_default_config = dict(
+coinrun_ppo_config = dict(
     env=dict(
         is_train=True,
         env_id='coinrun',
@@ -39,8 +39,8 @@
         ),
     ),
 )
-coinrun_ppo_default_config = EasyDict(coinrun_ppo_default_config)
-main_config = coinrun_ppo_default_config
+coinrun_ppo_config = EasyDict(coinrun_ppo_config)
+main_config = coinrun_ppo_config
 
 coinrun_ppo_create_config = dict(
     env=dict(

dizoo/procgen/entry/maze_dqn_config.py → dizoo/procgen/config/maze_dqn_config.py

@@ -1,6 +1,6 @@
 from easydict import EasyDict
 
-maze_dqn_default_config = dict(
+maze_dqn_config = dict(
     env=dict(
         collector_env_num=4,
         env_id='maze',
@@ -37,8 +37,8 @@
         ),
     ),
 )
-maze_dqn_default_config = EasyDict(maze_dqn_default_config)
-main_config = maze_dqn_default_config
+maze_dqn_config = EasyDict(maze_dqn_config)
+main_config = maze_dqn_config
 
 maze_dqn_create_config = dict(
     env=dict(