feature(whl):add trex example

ding/example/trex.py

@@ -0,0 +1,59 @@
+import gym
+from tensorboardX import SummaryWriter
+import copy
+import easydict
+import os
+from ditk import logging
+
+from ding.model import DQN
+from ding.policy import DQNPolicy
+from ding.envs import DingEnvWrapper, BaseEnvManagerV2
+from ding.data import DequeBuffer
+from ding.config import compile_config
+from ding.framework import task
+from ding.framework.context import OnlineRLContext
+from ding.framework.middleware import OffPolicyLearner, StepCollector, interaction_evaluator, \
+    eps_greedy_handler, CkptSaver, eps_greedy_masker, sqil_data_pusher, data_pusher
+from ding.utils import set_pkg_seed
+from dizoo.classic_control.cartpole.config.cartpole_trex_dqn_config import main_config, create_config
+from ding.entry import trex_collecting_data
+from ding.reward_model import create_reward_model
+
+
+def main():
+    logging.getLogger().setLevel(logging.INFO)
+    demo_arg = easydict.EasyDict({'cfg': [main_config, create_config], 'seed': 0})
+    trex_collecting_data(demo_arg)
+    cfg = compile_config(main_config, create_cfg=create_config, auto=True)
+
+    with task.start(async_mode=False, ctx=OnlineRLContext()):
+        collector_env = BaseEnvManagerV2(
+            env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.collector_env_num)],
+            cfg=cfg.env.manager
+        )
+
+        evaluator_env = BaseEnvManagerV2(
+            env_fn=[lambda: DingEnvWrapper(gym.make("CartPole-v0")) for _ in range(cfg.env.evaluator_env_num)],
+            cfg=cfg.env.manager
+        )
+
+        set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
+        model = DQN(**cfg.policy.model)
+        buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size)
+        policy = DQNPolicy(cfg.policy, model=model)
+
+        tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
+        reward_model = create_reward_model(copy.deepcopy(cfg), policy.collect_mode.get_attribute('device'), tb_logger)
+        reward_model.train()
+
+        task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env))
+        task.use(eps_greedy_handler(cfg))
+        task.use(StepCollector(cfg, policy.collect_mode, collector_env))
+        task.use(data_pusher(cfg, buffer_))
+        task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_, reward_model))
+        task.use(CkptSaver(cfg, policy, train_freq=100))
+        task.run()
+
+
+if __name__ == "__main__":
+    main()

dizoo/classic_control/cartpole/config/cartpole_trex_dqn_config.py

@@ -20,9 +20,11 @@
         checkpoint_step=100,
         learning_rate=1e-5,
         update_per_collect=1,
+        num_trajs=6,
+        num_snippets=6000,
         expert_model_path='abs model path',
         reward_model_path='abs data path + ./cartpole.params',
-        offline_data_path='abs data path',
+        data_path='abs data path',
     ),
     policy=dict(
         load_path='',