arena add test more envs

arena add test more envs

examples/arena/README.md

@@ -3,6 +3,7 @@
 
 ```bash
 pip install "openrl[selfplay]"
+pip install "pettingzoo[mpe]","pettingzoo[butterfly]"
 ```
 
 ### Usage
@@ -15,3 +16,11 @@ python run_arena.py
 ### Evaluate Google Research Football submissions for JiDi locally
 
 If you want to evaluate your Google Research Football submissions for JiDi locally, please try to use tizero as illustrated [here](foothttps://github.com/OpenRL-Lab/TiZero#evaluate-jidi-submissions-locally).
+
+### Evaluate more environments
+
+We also provide a script to evaluate more environments, including MPE, Go, Texas Holdem, Butterfly. You can run the script as follows:
+
+```shell
+python evaluate_more_envs.py
+```

examples/arena/evaluate_more_envs.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright 2023 The OpenRL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""""""
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright 2023 The OpenRL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""""""
+
+from pettingzoo.butterfly import cooperative_pong_v5
+from pettingzoo.classic import connect_four_v3, go_v5, texas_holdem_no_limit_v6
+from pettingzoo.mpe import simple_push_v3
+
+from examples.custom_env.rock_paper_scissors import RockPaperScissors
+from openrl.arena import make_arena
+from openrl.arena.agents.local_agent import LocalAgent
+from openrl.envs.PettingZoo.registration import register
+from openrl.envs.wrappers.pettingzoo_wrappers import RecordWinner
+
+
+def ConnectFourEnv(render_mode, **kwargs):
+    return connect_four_v3.env(render_mode)
+
+
+def RockPaperScissorsEnv(render_mode, **kwargs):
+    return RockPaperScissors(render_mode)
+
+
+def GoEnv(render_mode, **kwargs):
+    return go_v5.env(render_mode=render_mode, board_size=5, komi=7.5)
+
+
+def TexasHoldemEnv(render_mode, **kwargs):
+    return texas_holdem_no_limit_v6.env(render_mode=render_mode)
+
+
+# MPE
+def SimplePushEnv(render_mode, **kwargs):
+    return simple_push_v3.env(render_mode=render_mode)
+
+
+def CooperativePongEnv(render_mode, **kwargs):
+    return cooperative_pong_v5.env(render_mode=render_mode)
+
+
+def register_new_envs():
+    new_env_dict = {
+        "connect_four_v3": ConnectFourEnv,
+        "RockPaperScissors": RockPaperScissorsEnv,
+        "go_v5": GoEnv,
+        "texas_holdem_no_limit_v6": TexasHoldemEnv,
+        "simple_push_v3": SimplePushEnv,
+        "cooperative_pong_v5": CooperativePongEnv,
+    }
+
+    for env_id, env in new_env_dict.items():
+        register(env_id, env)
+    return new_env_dict.keys()
+
+
+def run_arena(
+    env_id: str,
+    parallel: bool = True,
+    seed=0,
+    total_games: int = 10,
+    max_game_onetime: int = 5,
+):
+    env_wrappers = [RecordWinner]
+
+    arena = make_arena(env_id, env_wrappers=env_wrappers, use_tqdm=False)
+
+    agent1 = LocalAgent("../selfplay/opponent_templates/random_opponent")
+    agent2 = LocalAgent("../selfplay/opponent_templates/random_opponent")
+
+    arena.reset(
+        agents={"agent1": agent1, "agent2": agent2},
+        total_games=total_games,
+        max_game_onetime=max_game_onetime,
+        seed=seed,
+    )
+    result = arena.run(parallel=parallel)
+    arena.close()
+    print(result)
+    return result
+
+
+def test_new_envs():
+    env_ids = register_new_envs()
+    seed = 0
+    for env_id in env_ids:
+        run_arena(env_id=env_id, seed=seed, parallel=False, total_games=1)
+
+
+if __name__ == "__main__":
+    test_new_envs()

examples/custom_env/rock_paper_scissors.py

@@ -182,6 +182,7 @@ def step(self, action):
             # handles stepping an agent which is already dead
             # accepts a None action for the one agent, and moves the agent_selection to
             # the next dead agent,  or if there are no more dead agents, to the next live agent
+            action = None
             self._was_dead_step(action)
             return
 

openrl/arena/games/two_player_game.py

@@ -31,9 +31,10 @@ def default_dispatch_func(
         players: List[str],
         agent_names: List[str],
     ) -> Dict[str, str]:
-        assert len(players) == len(
-            agent_names
-        ), "The number of players must be equal to the number of agents."
+        assert len(players) == len(agent_names), (
+            f"The number of players {len(players)} must be equal to the number of"
+            f" agents: {len(agent_names)}."
+        )
         assert len(players) == 2, "The number of players must be equal to 2."
         np_random.shuffle(agent_names)
         return dict(zip(players, agent_names))
@@ -49,20 +50,21 @@ def _run(self, env_fn: Callable, agents: List[BaseAgent]):
         for player, agent in player2agent.items():
             agent.reset(env, player)
         result = {}
+        truncation_dict = {}
         while True:
             termination = False
             info = {}
             for player_name in env.agent_iter():
                 observation, reward, termination, truncation, info = env.last()
-
-                if termination:
+                truncation_dict[player_name] = truncation
+                if termination or all(truncation_dict.values()):
                     break
                 action = player2agent[player_name].act(
                     player_name, observation, reward, termination, truncation, info
                 )
                 env.step(action)
 
-            if termination:
+            if termination or all(truncation_dict.values()):
                 assert "winners" in info, "The game is terminated but no winners."
                 assert "losers" in info, "The game is terminated but no losers."
 

openrl/envs/wrappers/pettingzoo_wrappers.py

@@ -96,8 +96,9 @@ def last(self, observe: bool = True):
 
         winners = None
         losers = None
+
         for agent in self.terminations:
-            if self.terminations[agent]:
+            if self.terminations[agent] or all(self.truncations):
                 if winners is None:
                     winners = self.get_winners()
                     losers = [player for player in self.agents if player not in winners]

openrl/selfplay/opponents/random_opponent.py

@@ -47,11 +47,20 @@ def _sample_random_action(
             action = []
 
             for obs, space in zip(observation, action_space):
-                mask = obs.get("action_mask", None)
-                action.append(space.sample(mask))
+                if termination or truncation:
+                    action.append(None)
+                else:
+                    if isinstance(obs, dict):
+                        mask = obs.get("action_mask", None)
+                    else:
+                        mask = None
+                    action.append(space.sample(mask))
         else:
-            mask = observation.get("action_mask", None)
-            action = action_space.sample(mask)
+            if termination or truncation:
+                action = None
+            else:
+                mask = observation.get("action_mask", None)
+                action = action_space.sample(mask)
         return action
 
     def _load(self, opponent_path: Union[str, Path]):

setup.py

@@ -71,11 +71,13 @@ def get_extra_requires() -> dict:
             "evaluate",
         ],
         "selfplay": ["ray[default]", "ray[serve]", "pettingzoo[classic]", "trueskill"],
+        "selfplay_test": ["pettingzoo[mpe]", "pettingzoo[butterfly]"],
         "retro": ["gym-retro"],
         "super_mario": ["gym-super-mario-bros"],
         "atari": ["gymnasium[atari]", "gymnasium[accept-rom-license]"],
     }
     req["test"].extend(req["selfplay"])
+    req["test"].extend(req["selfplay_test"])
     req["test"].extend(req["atari"])
     req["test"].extend(req["nlp_test"])
     return req

tests/test_arena/test_new_envs.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright 2023 The OpenRL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""""""
+import os
+import sys
+
+import pytest
+from pettingzoo.butterfly import cooperative_pong_v5
+from pettingzoo.classic import connect_four_v3, go_v5, texas_holdem_no_limit_v6
+from pettingzoo.mpe import simple_push_v3
+
+from examples.custom_env.rock_paper_scissors import RockPaperScissors
+from openrl.arena import make_arena
+from openrl.arena.agents.local_agent import LocalAgent
+from openrl.envs.PettingZoo.registration import register
+from openrl.envs.wrappers.pettingzoo_wrappers import RecordWinner
+
+
+def ConnectFourEnv(render_mode, **kwargs):
+    return connect_four_v3.env(render_mode)
+
+
+def RockPaperScissorsEnv(render_mode, **kwargs):
+    return RockPaperScissors(render_mode)
+
+
+def GoEnv(render_mode, **kwargs):
+    return go_v5.env(render_mode=render_mode, board_size=5, komi=7.5)
+
+
+def TexasHoldemEnv(render_mode, **kwargs):
+    return texas_holdem_no_limit_v6.env(render_mode=render_mode)
+
+
+# MPE
+def SimplePushEnv(render_mode, **kwargs):
+    return simple_push_v3.env(render_mode=render_mode)
+
+
+def CooperativePongEnv(render_mode, **kwargs):
+    return cooperative_pong_v5.env(render_mode=render_mode)
+
+
+def register_new_envs():
+    new_env_dict = {
+        "connect_four_v3": ConnectFourEnv,
+        "RockPaperScissors": RockPaperScissorsEnv,
+        "go_v5": GoEnv,
+        "texas_holdem_no_limit_v6": TexasHoldemEnv,
+        "simple_push_v3": SimplePushEnv,
+        "cooperative_pong_v5": CooperativePongEnv,
+    }
+
+    for env_id, env in new_env_dict.items():
+        register(env_id, env)
+    return new_env_dict.keys()
+
+
+def run_arena(
+    env_id: str,
+    parallel: bool = True,
+    seed=0,
+    total_games: int = 10,
+    max_game_onetime: int = 5,
+):
+    env_wrappers = [RecordWinner]
+
+    arena = make_arena(env_id, env_wrappers=env_wrappers, use_tqdm=False)
+
+    agent1 = LocalAgent("./examples/selfplay/opponent_templates/random_opponent")
+    agent2 = LocalAgent("./examples/selfplay/opponent_templates/random_opponent")
+
+    arena.reset(
+        agents={"agent1": agent1, "agent2": agent2},
+        total_games=total_games,
+        max_game_onetime=max_game_onetime,
+        seed=seed,
+    )
+    result = arena.run(parallel=parallel)
+    arena.close()
+    return result
+
+
+@pytest.mark.unittest
+def test_new_envs():
+    env_ids = register_new_envs()
+    seed = 0
+    for env_id in env_ids:
+        run_arena(env_id=env_id, seed=seed, parallel=False, total_games=1)
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main(["-sv", os.path.basename(__file__)]))