feat: copied and pasteed multi agent env from metadrive examples

script/main.py

@@ -1,11 +1,83 @@
-from metadrive.envs.metadrive_env import MetaDriveEnv
-import gymnasium as gym
-from metadrive.envs.gym_wrapper import createGymWrapper # import the wrapper
+#!/usr/bin/env python
+"""
+This script demonstrates how to setup the Multi-agent RL environments.
 
-env = createGymWrapper(MetaDriveEnv)(config={"use_render": True}) # wrap the environment
-obs = env.reset()
-for i in range(1000):
-    obs, reward, done, info = env.step(env.action_space.sample()) # the return value contains no truncate
-    if done:
+
+Usage: python -m metadrive.examples.drive_in_multi_agent_env --env pgma
+
+Options for --env argument:
+    (1) roundabout
+    (2) intersection
+    (3) tollgate
+    (4) bottleneck
+    (5) parkinglot
+    (6) pgma
+
+"""
+import argparse
+from metadrive.component.sensors.rgb_camera import RGBCamera
+from metadrive import (
+    MultiAgentMetaDrive, MultiAgentTollgateEnv, MultiAgentBottleneckEnv, MultiAgentIntersectionEnv,
+    MultiAgentRoundaboutEnv, MultiAgentParkingLotEnv
+)
+from metadrive.constants import HELP_MESSAGE
+from metadrive.policy.idm_policy import ManualControllableIDMPolicy
+
+if __name__ == "__main__":
+    envs = dict(
+        roundabout=MultiAgentRoundaboutEnv,
+        intersection=MultiAgentIntersectionEnv,
+        tollgate=MultiAgentTollgateEnv,
+        bottleneck=MultiAgentBottleneckEnv,
+        parkinglot=MultiAgentParkingLotEnv,
+        pgma=MultiAgentMetaDrive
+    )
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--env", type=str, default="roundabout", choices=list(envs.keys()))
+    parser.add_argument("--top_down", "--topdown", action="store_true")
+    args = parser.parse_args()
+    env_cls_name = args.env
+    extra_args = dict(film_size=(800, 800)) if args.top_down else {}
+    assert env_cls_name in envs.keys(), "No environment named {}, argument accepted: \n" \
+                                        "(1) roundabout\n" \
+                                        "(2) intersection\n" \
+                                        "(3) tollgate\n" \
+                                        "(4) bottleneck\n" \
+                                        "(5) parkinglot\n" \
+                                        "(6) pgma" \
+        .format(env_cls_name)
+    env = envs[env_cls_name](
+        {
+            "use_render": True if not args.top_down else False,
+            "crash_done": False,
+            "sensors": dict(rgb_camera=(RGBCamera, 400, 300)),
+            "interface_panel": ["rgb_camera", "dashboard"],
+            "agent_policy": ManualControllableIDMPolicy
+        }
+    )
+    try:
         env.reset()
-env.close()
+        # if env.current_track_agent:
+        #     env.current_track_agent.expert_takeover = True
+        print(HELP_MESSAGE)
+        env.switch_to_third_person_view()  # Default is in Top-down view, we switch to Third-person view.
+        for i in range(1, 10000000000):
+            o, r, tm, tc, info = env.step({agent_id: [0, 0] for agent_id in env.agents.keys()})
+            env.render(
+                **extra_args,
+                mode="top_down" if args.top_down else None,
+                text={
+                    "Quit": "ESC",
+                    "Number of existing vehicles": len(env.agents),
+                    "Tracked agent (Press Q)": env.engine.agent_manager.object_to_agent(env.current_track_agent.id),
+                    "Keyboard Control": "W,A,S,D",
+                    # "Auto-Drive (Switch mode: T)": "on" if env.current_track_agent.expert_takeover else "off",
+                } if not args.top_down else {}
+            )
+            if tm["__all__"]:
+                env.reset()
+                # if env.current_track_agent:
+                #     env.current_track_agent.expert_takeover = True
+    finally:
+        env.close()