merge dm_control to gymnasium

merge dm_control to gymnasium

examples/dm_control/README.md

@@ -1,6 +1,6 @@
 ## Installation
 ```bash
-pip install shimmy[dm-control]
+pip install "shimmy[dm-control]"
 ```
 
 ## Usage

examples/dm_control/train_ppo.py

@@ -1,10 +1,15 @@
 import numpy as np
+import torch
 from gymnasium.wrappers import FlattenObservation
 
 from openrl.configs.config import create_config_parser
 from openrl.envs.common import make
 from openrl.envs.wrappers.base_wrapper import BaseWrapper
-from openrl.envs.wrappers.extra_wrappers import FrameSkip, GIFWrapper
+from openrl.envs.wrappers.extra_wrappers import (
+    ConvertEmptyBoxWrapper,
+    FrameSkip,
+    GIFWrapper,
+)
 from openrl.modules.common import PPONet as Net
 from openrl.runners.common import PPOAgent as Agent
 
@@ -18,15 +23,15 @@ def train():
     cfg = cfg_parser.parse_args(["--config", "ppo.yaml"])
 
     # create environment, set environment parallelism to 64
+    env_num = 64
     env = make(
         env_name,
-        env_num=64,
-        cfg=cfg,
+        env_num=env_num,
         asynchronous=True,
-        env_wrappers=[FrameSkip, FlattenObservation],
+        env_wrappers=[FrameSkip, FlattenObservation, ConvertEmptyBoxWrapper],
     )
 
-    net = Net(env, cfg=cfg, device="cuda")
+    net = Net(env, cfg=cfg, device="cuda" if torch.cuda.is_available() else "cpu")
     # initialize the trainer
     agent = Agent(
         net,
@@ -44,18 +49,18 @@ def evaluation():
     # begin to test
     # Create an environment for testing and set the number of environments to interact with to 4. Set rendering mode to group_rgb_array.
     render_mode = "group_rgb_array"
+
     env = make(
         env_name,
         render_mode=render_mode,
         env_num=4,
         asynchronous=True,
-        env_wrappers=[FrameSkip, FlattenObservation],
-        cfg=cfg,
+        env_wrappers=[FrameSkip, FlattenObservation, ConvertEmptyBoxWrapper],
     )
     # Wrap the environment with GIFWrapper to record the GIF, and set the frame rate to 5.
     env = GIFWrapper(env, gif_path="./new.gif", fps=5)
 
-    net = Net(env, cfg=cfg, device="cuda")
+    net = Net(env, cfg=cfg, device="cuda" if torch.cuda.is_available() else "cpu")
     # initialize the trainer
     agent = Agent(
         net,

openrl/envs/common/registration.py

@@ -78,13 +78,6 @@ def make(
             env_fns = make_snake_envs(
                 id=id, env_num=env_num, render_mode=convert_render_mode, **kwargs
             )
-
-        elif id.startswith("dm_control/"):
-            from openrl.envs.dmc import make_dmc_envs
-
-            env_fns = make_dmc_envs(
-                id=id, env_num=env_num, render_mode=convert_render_mode, **kwargs
-            )
         elif id.startswith("GymV21Environment-v0:") or id.startswith(
             "GymV26Environment-v0:"
         ):