demo(nyz): add naive PWIL demo

dizoo/classic_control/cartpole/entry/cartpole_dqn_pwil_main.py

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+from easydict import EasyDict
+from copy import deepcopy
+
+from dizoo.classic_control.cartpole.config.cartpole_ppo_offpolicy_config import cartpole_ppo_offpolicy_config, cartpole_ppo_offpolicy_create_config  # noqa
+from dizoo.classic_control.cartpole.config.cartpole_dqn_config import cartpole_dqn_config, cartpole_dqn_create_config
+from ding.entry import serial_pipeline, collect_demo_data, serial_pipeline_reward_model_offpolicy
+
+
+def cartpole_dqn_pwil_main():
+    reward_model_config = {
+        'type': 'pwil',
+        's_size': 4,
+        'a_size': 2,
+        'sample_size': 500,
+    }
+
+    # train a expert policy (PPO offpolicy)
+    reward_model_config = EasyDict(reward_model_config)
+    config = deepcopy(cartpole_ppo_offpolicy_config), deepcopy(cartpole_ppo_offpolicy_create_config)
+    expert_policy = serial_pipeline(config, seed=0)
+
+    # (optional) collect expert demo data
+    collect_count = 10000
+    expert_data_path = 'expert_data.pkl'
+    state_dict = expert_policy.collect_mode.state_dict()
+    config = deepcopy(cartpole_ppo_offpolicy_config), deepcopy(cartpole_ppo_offpolicy_create_config)
+    collect_demo_data(
+        config, seed=0, state_dict=state_dict, expert_data_path=expert_data_path, collect_count=collect_count
+    )
+
+    # irl + rl training
+    cp_cartpole_dqn_config = deepcopy(cartpole_dqn_config)
+    cp_cartpole_dqn_create_config = deepcopy(cartpole_dqn_create_config)
+    cp_cartpole_dqn_create_config.reward_model = dict(type=reward_model_config.type)
+    reward_model_config['expert_data_path'] = expert_data_path
+    cp_cartpole_dqn_config.exp_name = 'cartpole_dqn_pwil'
+    cp_cartpole_dqn_config.reward_model = reward_model_config
+    cp_cartpole_dqn_config.policy.collect.n_sample = 128
+
+    serial_pipeline_reward_model_offpolicy((cp_cartpole_dqn_config, cp_cartpole_dqn_create_config), seed=0)
+
+
+if __name__ == "__main__":
+    cartpole_dqn_pwil_main()