feature(zt): add metadrive-simulator env and related onppo config

dizoo/metadrive/config/test_ppo.py

@@ -0,0 +1,107 @@
+
+from easydict import EasyDict
+from functools import partial
+from tensorboardX import SummaryWriter
+
+from ding.envs import BaseEnvManager, SyncSubprocessEnvManager
+from ding.config import compile_config
+from ding.model.template import  VAC
+from ding.policy import PPOPolicy
+from ding.worker import SampleSerialCollector, InteractionSerialEvaluator, BaseLearner
+from dizoo.metadrive.env.drive_env import MetaDrivePPOOriginEnv
+from dizoo.metadrive.env.drive_wrapper import DriveEnvWrapper
+import torch 
+
+# ckpt dir: 
+model_dir = None #'dizoo/metadrive/config/demo.pth.tar'
+
+metadrive_basic_config = dict(
+    exp_name='test_ppo_metadrive',
+    env=dict(
+        metadrive=dict(
+            use_render = True,
+            traffic_density=0.10,
+            map = 'XSOS',
+            horizon = 4000, #20000
+            driving_reward = 1.0,
+            speed_reward = 0.10,
+            out_of_road_penalty = 40.0,
+            crash_vehicle_penalty = 40.0,
+            decision_repeat=20,
+            use_lateral_reward = False,
+            out_of_route_done = True,
+            ),
+        manager=dict(
+            shared_memory=False,
+            max_retry=2,
+            context='spawn',
+        ),
+        n_evaluator_episode=16,
+        stop_value=99999,
+        collector_env_num=1,
+        evaluator_env_num=1,
+    ),
+    policy=dict(
+        cuda=True,
+        action_space='continuous',
+        model=dict(
+            #obs_shape=[5, 200, 200],
+            obs_shape=[5, 84, 84],
+            action_shape=2,
+            action_space='continuous',
+            bound_type='tanh',
+            encoder_hidden_size_list=[128, 128, 64],
+        ),
+        learn=dict(
+            epoch_per_collect=10,
+            batch_size=64,
+            learning_rate=3e-4,
+            entropy_weight = 0.001,
+            value_weight=0.5,
+            clip_ratio = 0.02,
+            adv_norm=False,
+            value_norm=True,
+            grad_clip_value=10,
+        ),
+        collect=dict(
+            n_sample=1000,
+        ),
+        eval=dict(
+            evaluator=dict(
+                eval_freq=1000,
+            ),
+        ),
+    ),
+)
+
+main_config = EasyDict(metadrive_basic_config)
+def wrapped_env(env_cfg, wrapper_cfg=None):
+    return DriveEnvWrapper(MetaDrivePPOOriginEnv(env_cfg), wrapper_cfg)
+
+def main(cfg):
+    cfg = compile_config(
+        cfg, BaseEnvManager, PPOPolicy, BaseLearner, SampleSerialCollector, InteractionSerialEvaluator
+    )
+    collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
+    evaluator_env = BaseEnvManager(
+        env_fn=[partial(wrapped_env, cfg.env.metadrive) for _ in range(evaluator_env_num)],
+        cfg=cfg.env.manager,
+    )
+    model = VAC(**cfg.policy.model)
+    policy = PPOPolicy(cfg.policy, model=model)
+    if model_dir is not None:
+        policy._load_state_dict_collect(torch.load(model_dir, map_location='cpu'))
+    tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name))
+    learner = BaseLearner(cfg.policy.learn.learner, policy.learn_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
+    )
+    learner.call_hook('before_run')
+
+    stop, rate = evaluator.eval()
+    evaluator.close()
+    learner.close()
+
+
+if __name__ == '__main__':
+    main(main_config)

dizoo/metadrive/config/train_ppo.py

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+import metadrive
+import gym
+from easydict import EasyDict
+from functools import partial
+from tensorboardX import SummaryWriter
+
+from ding.envs import BaseEnvManager, SyncSubprocessEnvManager
+from ding.config import compile_config
+from ding.model.template import QAC, VAC
+from ding.policy import PPOPolicy
+from ding.worker import SampleSerialCollector, InteractionSerialEvaluator, BaseLearner
+from dizoo.metadrive.env.drive_env import MetaDrivePPOOriginEnv
+from dizoo.metadrive.env.drive_wrapper import DriveEnvWrapper
+metadrive_basic_config = dict(
+    exp_name='train_ppo_metadrive',
+    env=dict(
+        metadrive=dict(
+            use_render = False,
+            traffic_density=0.10,
+            map = 'XSOS',
+            horizon = 4000, #20000
+            driving_reward = 1.0,
+            speed_reward = 0.1,
+            out_of_road_penalty = 40.0,
+            crash_vehicle_penalty = 40.0,
+            decision_repeat=20,
+            use_lateral_reward=False,
+            out_of_route_done = True,
+            ),
+        manager=dict(
+            shared_memory=False,
+            max_retry=2,
+            context='spawn',
+        ),
+        n_evaluator_episode=16,
+        stop_value=99999,
+        collector_env_num=1,
+        evaluator_env_num=1,
+    ),
+    policy=dict(
+        cuda=True,
+        action_space='continuous',
+        model=dict(
+            #obs_shape=[5, 200, 200],
+            obs_shape=[5, 84, 84],
+            action_shape=2,
+            action_space='continuous',
+            bound_type='tanh',
+            encoder_hidden_size_list=[128, 128, 64],
+        ),
+        learn=dict(
+            epoch_per_collect=10,
+            batch_size=64,
+            learning_rate=3e-4,
+            entropy_weight = 0.001,
+            value_weight=0.5,
+            clip_ratio = 0.02,
+            adv_norm=False,
+            value_norm=True,
+            grad_clip_value=10,
+        ),
+        collect=dict(
+            n_sample=3000, #1000
+        ),
+        eval=dict(
+            evaluator=dict(
+                eval_freq=1000,
+            ),
+        ),
+    ),
+)
+
+main_config = EasyDict(metadrive_basic_config)
+
+def wrapped_env(env_cfg, wrapper_cfg=None):
+    return DriveEnvWrapper(MetaDrivePPOOriginEnv(env_cfg), wrapper_cfg)
+
+
+def main(cfg):
+    cfg = compile_config(
+        cfg, SyncSubprocessEnvManager, PPOPolicy, BaseLearner, SampleSerialCollector, InteractionSerialEvaluator
+    )
+
+    collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
+    collector_env = SyncSubprocessEnvManager(
+        env_fn=[partial(wrapped_env, cfg.env.metadrive) for _ in range(collector_env_num)],
+        cfg=cfg.env.manager,
+    )
+    evaluator_env = SyncSubprocessEnvManager(
+        env_fn=[partial(wrapped_env, cfg.env.metadrive) for _ in range(evaluator_env_num)],
+        cfg=cfg.env.manager,
+    )
+
+
+    model = VAC(**cfg.policy.model)
+    policy = PPOPolicy(cfg.policy, model=model)
+    tb_logger = SummaryWriter('./log/{}/'.format(cfg.exp_name))
+    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
+    )
+
+    learner.call_hook('before_run')
+
+    while True:
+        if evaluator.should_eval(learner.train_iter):
+            stop, rate = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
+            if stop:
+                break
+        # Sampling data from environments
+        new_data = collector.collect(cfg.policy.collect.n_sample, train_iter=learner.train_iter)
+        learner.train(new_data, collector.envstep)
+    collector.close()
+    evaluator.close()
+    learner.close()
+
+
+if __name__ == '__main__':
+    main(main_config)