fix(nyz): update ptz to latest version

ding/envs/env_manager/subprocess_env_manager.py

@@ -5,6 +5,7 @@
 import platform
 import time
 import copy
+import gymnasium
 import gym
 import traceback
 import torch
@@ -104,7 +105,7 @@ def _create_state(self) -> None:
         self._reset_param = {i: {} for i in range(self.env_num)}
         if self._shared_memory:
             obs_space = self._observation_space
-            if isinstance(obs_space, gym.spaces.Dict):
+            if isinstance(obs_space, (gym.spaces.Dict, gymnasium.spaces.Dict)):
                 # For multi_agent case, such as multiagent_mujoco and petting_zoo mpe.
                 # Now only for the case that each agent in the team have the same obs structure
                 # and corresponding shape.

dizoo/petting_zoo/envs/petting_zoo_simple_spread_env.py

@@ -1,5 +1,5 @@
 from typing import Any, List, Union, Optional, Dict
-import gym
+import gymnasium as gym
 import numpy as np
 import pettingzoo
 from functools import reduce
@@ -8,6 +8,9 @@
 from ding.torch_utils import to_ndarray, to_list
 from ding.envs.common.common_function import affine_transform
 from ding.utils import ENV_REGISTRY, import_module
+from pettingzoo.utils.conversions import parallel_wrapper_fn
+from pettingzoo.mpe._mpe_utils.simple_env import SimpleEnv, make_env
+from pettingzoo.mpe.simple_spread.simple_spread import Scenario
 
 
 @ENV_REGISTRY.register('petting_zoo')
@@ -52,13 +55,14 @@ def reset(self) -> np.ndarray:
         # if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed:
         #     np_seed = 100 * np.random.randint(1, 1000)
         #     self._env.seed(self._seed + np_seed)
-        if hasattr(self, '_seed'):
-            self._env.seed(self._seed)
         if self._replay_path is not None:
             self._env = gym.wrappers.Monitor(
                 self._env, self._replay_path, video_callable=lambda episode_id: True, force=True
             )
-        obs = self._env.reset()
+        if hasattr(self, '_seed'):
+            obs = self._env.reset(seed=self._seed)
+        else:
+            obs = self._env.reset()
         if not self._init_flag:
             self._agents = self._env.agents
 
@@ -69,7 +73,7 @@ def reset(self) -> np.ndarray:
             elif isinstance(single_agent_obs_space, gym.spaces.Discrete):
                 self._action_dim = (single_agent_obs_space.n, )
             else:
-                raise Exception('Only support `Box` or `Discrte` obs space for single agent.')
+                raise Exception('Only support `Box` or `Discrete` obs space for single agent.')
 
             # only for env 'simple_spread_v2', n_agent = 5
             # now only for the case that each agent in the team have the same obs structure and corresponding shape.
@@ -173,7 +177,7 @@ def step(self, action: np.ndarray) -> BaseEnvTimestep:
                     action[agent], min_val=self.action_space[agent].low, max_val=self.action_space[agent].high
                 )
 
-        obs, rew, done, info = self._env.step(action)
+        obs, rew, done, trunc, info = self._env.step(action)
         obs_n = self._process_obs(obs)
         rew_n = np.array([sum([rew[agent] for agent in self._agents])])
         # collide_sum = 0
@@ -308,18 +312,13 @@ def reward_space(self) -> gym.spaces.Space:
         return self._reward_space
 
 
-from pettingzoo.utils.conversions import parallel_wrapper_fn
-from pettingzoo.mpe._mpe_utils.simple_env import SimpleEnv, make_env
-from pettingzoo.mpe.scenarios.simple_spread import Scenario
-
-
 class simple_spread_raw_env(SimpleEnv):
 
     def __init__(self, N=3, local_ratio=0.5, max_cycles=25, continuous_actions=False):
         assert 0. <= local_ratio <= 1., "local_ratio is a proportion. Must be between 0 and 1."
         scenario = Scenario()
         world = scenario.make_world(N)
-        super().__init__(scenario, world, max_cycles, continuous_actions, local_ratio)
+        super().__init__(scenario, world, max_cycles, continuous_actions=continuous_actions, local_ratio=local_ratio)
         self.metadata['name'] = "simple_spread_v2"
 
     def _execute_world_step(self):

setup.py

@@ -78,7 +78,7 @@
         'mpire>=2.3.5',
         'pynng',
         'redis',
-        'pettingzoo==1.12.0',
+        'pettingzoo',
         'DI-treetensor>=0.3.0',
         'DI-toolkit>=0.0.2',
         'hbutils>=0.5.0',