feature(wrh): add taxi env latest version and dqn config (opendilab#807)

* update taxi env

README.md

@@ -324,7 +324,7 @@ P.S: The `.py` file in `Runnable Demo` can be found in `dizoo`
 | 37 |                       [tabmwp](https://promptpg.github.io/explore.html)                       |                                                                                      ![discrete](https://img.shields.io/badge/-discrete-brightgreen)                                                                                      |                                ![original](./dizoo/tabmwp/tabmwp.jpeg)                                |                                                                                         [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/tabmwp) <br> env tutorial <br> 环境指南                                                                                         |
 | 38 |            [frozen_lake](https://gymnasium.farama.org/environments/toy_text/frozen_lake)      |                                                                                      ![discrete](https://img.shields.io/badge/-discrete-brightgreen)                                                                                      |                                ![original](./dizoo/frozen_lake/FrozenLake.gif)                        |                                                                                         [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/frozen_lake) <br> env tutorial <br> 环境指南                                                                                         |
 | 39 | [ising_model](https://github.com/mlii/mfrl/tree/master/examples/ising_model)                  |                            ![discrete](https://img.shields.io/badge/-discrete-brightgreen) ![marl](https://img.shields.io/badge/-MARL-yellow)                                                                                             |                                ![original](./dizoo/ising_env/ising_env.gif)                           | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/ising_env) <br> env tutorial <br> [环境指南](https://di-engine-docs.readthedocs.io/zh_CN/latest/13_envs/ising_model_zh.html) |
-| 40 | [taxi](https://www.gymlibrary.dev/environments/toy_text/taxi/)                  |                            ![discrete](https://img.shields.io/badge/-discrete-brightgreen)                                                                                 |                                ![original](./dizoo/taxi/Taxi-v3_episode_0.gif)                           | dizoo link <br> env tutorial <br> 环境指南 |
+| 40 | [taxi](https://www.gymlibrary.dev/environments/toy_text/taxi/)                  |                            ![discrete](https://img.shields.io/badge/-discrete-brightgreen)                                                                                 |                                ![original](./dizoo/taxi/Taxi-v3_episode_0.gif)                           | [dizoo link](https://github.com/opendilab/DI-engine/tree/main/dizoo/taxi/envs) <br> [env tutorial](https://di-engine-docs.readthedocs.io/en/latest/13_envs/taxi.html) <br> [环境指南](https://di-engine-docs.readthedocs.io/zh-cn/latest/13_envs/taxi_zh.html) |
 
 
 

dizoo/taxi/config/taxi_dqn_config.py

@@ -1,39 +1,45 @@
 from easydict import EasyDict
 
 taxi_dqn_config = dict(
-    exp_name='taxi_seed0',
+    exp_name='taxi_dqn_seed0',
     env=dict(
         collector_env_num=8,
-        evaluator_env_num=8,  
-        n_evaluator_episode=10,
-        max_episode_steps=300,
-        env_id="Taxi-v3"
+        evaluator_env_num=8,
+        n_evaluator_episode=8,   
+        stop_value=20,           
+        max_episode_steps=60,    
+        env_id="Taxi-v3" 
     ),
     policy=dict(
         cuda=True,
-        load_path="./taxi_dqn_seed0/ckpt/ckpt_best.pth.tar",
         model=dict(
-            obs_shape=4,    
+            obs_shape=34,
             action_shape=6,
-            encoder_hidden_size_list=[256, 128, 64]
+            encoder_hidden_size_list=[128, 128]
         ),
+        random_collect_size=5000,
         nstep=3,
-        discount_factor=0.98,
+        discount_factor=0.99,
         learn=dict(
-            update_per_collect=5,
-            batch_size=128,
-            learning_rate=0.001,
+            update_per_collect=10,
+            batch_size=64,
+            learning_rate=0.0001,
+            learner=dict(
+                hook=dict(
+                    log_show_after_iter=1000,
+                )
+            ),
         ),
-        collect=dict(n_sample=10),
-        eval=dict(evaluator=dict(eval_freq=5, )),
+        collect=dict(n_sample=32),
+        eval=dict(evaluator=dict(eval_freq=1000, )), 
         other=dict(
             eps=dict(
               type="linear",
-              start=0.8,
-              end=0.1,
-              decay=10000  
-            ),
-            replay_buffer=dict(replay_buffer_size=20000,),
+              start=1,
+              end=0.05,
+              decay=3000000                             
+            ),                                      
+            replay_buffer=dict(replay_buffer_size=100000,),  
         ),
     )
 )
@@ -55,4 +61,4 @@
 
 if __name__ == "__main__":
     from ding.entry import serial_pipeline
-    serial_pipeline((main_config, create_config), max_env_step=5000, seed=0)
+    serial_pipeline((main_config, create_config), max_env_step=3000000, seed=0)

dizoo/taxi/envs/taxi_env.py

@@ -93,8 +93,8 @@ def step(self, action: np.ndarray) -> BaseEnvTimestep:
     def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
         if replay_path is None:
             replay_path = './video'
-            if not os.path.exists(replay_path):
-                os.makedirs(replay_path)
+        if not os.path.exists(replay_path):
+            os.makedirs(replay_path)
         self._replay_path = replay_path
         self._save_replay = True
         self._save_replay_count = 0
@@ -118,7 +118,11 @@ def random_action(self) -> np.ndarray:
     #todo encode the state into a vector    
     def _encode_taxi(self, obs: np.ndarray) -> np.ndarray:
         taxi_row, taxi_col, passenger_location, destination = self._env.unwrapped.decode(obs)
-        return to_ndarray([taxi_row, taxi_col, passenger_location, destination])
+        encoded_obs = np.zeros(34)
+        encoded_obs[5 * taxi_row + taxi_col] = 1
+        encoded_obs[25 + passenger_location] = 1
+        encoded_obs[30 + destination] = 1
+        return to_ndarray(encoded_obs)
 
     @property
     def observation_space(self) -> Space:

dizoo/taxi/envs/test_taxi_env.py

@@ -16,7 +16,7 @@ def test_naive(self):
         env.seed(314, dynamic_seed=False)
         assert env._seed == 314
         obs = env.reset()
-        assert obs.shape == (4, )
+        assert obs.shape == (34, )
         for _ in range(5):
             env.reset()
             np.random.seed(314)
@@ -32,7 +32,7 @@ def test_naive(self):
                 print(f"Your timestep in wrapped mode is: {timestep}")
                 assert isinstance(timestep.obs, np.ndarray)
                 assert isinstance(timestep.done, bool)
-                assert timestep.obs.shape == (4, )
+                assert timestep.obs.shape == (34, )
                 assert timestep.reward.shape == (1, )
                 assert timestep.reward >= env.reward_space.low
                 assert timestep.reward <= env.reward_space.high