feature/provide access to wrapped attr

pettingzoo/atari/base_atari_env.py

@@ -171,7 +171,9 @@ def __init__(
 
     def _seed(self, seed=None):
         if seed is None:
-            _, seed = seeding.np_random()
+            self.np_random, seed = seeding.np_random()
+        else:
+            self.np_random, seed = seeding.np_random(seed)
         self.ale.setInt(b"random_seed", seed)
         self.ale.loadROM(self.rom_path)
         self.ale.setMode(self.mode)

pettingzoo/utils/wrappers/base.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import warnings
 from typing import Any
 
 import gymnasium.spaces
@@ -19,72 +18,12 @@ def __init__(self, env: AECEnv[AgentID, ObsType, ActionType]):
         super().__init__()
         self.env = env
 
-        try:
-            self.possible_agents = self.env.possible_agents
-        except AttributeError:
-            pass
-
-        self.metadata = self.env.metadata
-
-        # we don't want these defined as we don't want them used before they are gotten
-
-        # self.agent_selection = self.env.agent_selection
-
-        # self.rewards = self.env.rewards
-        # self.dones = self.env.dones
-
-        # we don't want to care one way or the other whether environments have an infos or not before reset
-        try:
-            self.infos = self.env.infos
-        except AttributeError:
-            pass
-
-        # Not every environment has the .state_space attribute implemented
-        try:
-            self.state_space = (
-                self.env.state_space  # pyright: ignore[reportGeneralTypeIssues]
-            )
-        except AttributeError:
-            pass
-
     def __getattr__(self, name: str) -> Any:
         """Returns an attribute with ``name``, unless ``name`` starts with an underscore."""
-        if name.startswith("_"):
+        if name.startswith("_") and name != "_cumulative_rewards":
             raise AttributeError(f"accessing private attribute '{name}' is prohibited")
         return getattr(self.env, name)
 
-    @property
-    def observation_spaces(self) -> dict[AgentID, gymnasium.spaces.Space]:
-        warnings.warn(
-            "The `observation_spaces` dictionary is deprecated. Use the `observation_space` function instead."
-        )
-        try:
-            return {
-                agent: self.observation_space(agent) for agent in self.possible_agents
-            }
-        except AttributeError as e:
-            raise AttributeError(
-                "The base environment does not have an `observation_spaces` dict attribute. Use the environment's `observation_space` method instead"
-            ) from e
-
-    @property
-    def action_spaces(self) -> dict[AgentID, gymnasium.spaces.Space]:
-        warnings.warn(
-            "The `action_spaces` dictionary is deprecated. Use the `action_space` function instead."
-        )
-        try:
-            return {agent: self.action_space(agent) for agent in self.possible_agents}
-        except AttributeError as e:
-            raise AttributeError(
-                "The base environment does not have an action_spaces dict attribute. Use the environment's `action_space` method instead"
-            ) from e
-
-    def observation_space(self, agent: AgentID) -> gymnasium.spaces.Space:
-        return self.env.observation_space(agent)
-
-    def action_space(self, agent: AgentID) -> gymnasium.spaces.Space:
-        return self.env.action_space(agent)
-
     @property
     def unwrapped(self) -> AECEnv:
         return self.env.unwrapped
@@ -98,14 +37,6 @@ def render(self) -> None | np.ndarray | str | list:
     def reset(self, seed: int | None = None, options: dict | None = None):
         self.env.reset(seed=seed, options=options)
 
-        self.agent_selection = self.env.agent_selection
-        self.rewards = self.env.rewards
-        self.terminations = self.env.terminations
-        self.truncations = self.env.truncations
-        self.infos = self.env.infos
-        self.agents = self.env.agents
-        self._cumulative_rewards = self.env._cumulative_rewards
-
     def observe(self, agent: AgentID) -> ObsType | None:
         return self.env.observe(agent)
 
@@ -115,13 +46,11 @@ def state(self) -> np.ndarray:
     def step(self, action: ActionType) -> None:
         self.env.step(action)
 
-        self.agent_selection = self.env.agent_selection
-        self.rewards = self.env.rewards
-        self.terminations = self.env.terminations
-        self.truncations = self.env.truncations
-        self.infos = self.env.infos
-        self.agents = self.env.agents
-        self._cumulative_rewards = self.env._cumulative_rewards
+    def observation_space(self, agent: AgentID) -> gymnasium.spaces.Space:
+        return self.env.observation_space(agent)
+
+    def action_space(self, agent: AgentID) -> gymnasium.spaces.Space:
+        return self.env.action_space(agent)
 
     def __str__(self) -> str:
         """Returns a name which looks like: "max_observation<space_invaders_v1>"."""

pettingzoo/utils/wrappers/base_parallel.py

@@ -1,40 +1,26 @@
 from __future__ import annotations
 
-import warnings
-
 import gymnasium.spaces
 import numpy as np
-from gymnasium.utils import seeding
 
 from pettingzoo.utils.env import ActionType, AgentID, ObsType, ParallelEnv
 
 
 class BaseParallelWrapper(ParallelEnv[AgentID, ObsType, ActionType]):
     def __init__(self, env: ParallelEnv[AgentID, ObsType, ActionType]):
+        super().__init__()
         self.env = env
 
-        self.metadata = env.metadata
-        try:
-            self.possible_agents = env.possible_agents
-        except AttributeError:
-            pass
-
-        # Not every environment has the .state_space attribute implemented
-        try:
-            self.state_space = (
-                self.env.state_space  # pyright: ignore[reportGeneralTypeIssues]
-            )
-        except AttributeError:
-            pass
+    def __getattr__(self, name: str):
+        """Returns an attribute with ``name``, unless ``name`` starts with an underscore."""
+        if name.startswith("_"):
+            raise AttributeError(f"accessing private attribute '{name}' is prohibited")
+        return getattr(self.env, name)
 
     def reset(
         self, seed: int | None = None, options: dict | None = None
     ) -> tuple[dict[AgentID, ObsType], dict[AgentID, dict]]:
-        self.np_random, _ = seeding.np_random(seed)
-
-        res, info = self.env.reset(seed=seed, options=options)
-        self.agents = self.env.agents
-        return res, info
+        return self.env.reset(seed=seed, options=options)
 
     def step(
         self, actions: dict[AgentID, ActionType]
@@ -45,9 +31,7 @@ def step(
         dict[AgentID, bool],
         dict[AgentID, dict],
     ]:
-        res = self.env.step(actions)
-        self.agents = self.env.agents
-        return res
+        return self.env.step(actions)
 
     def render(self) -> None | np.ndarray | str | list:
         return self.env.render()
@@ -62,32 +46,6 @@ def unwrapped(self) -> ParallelEnv:
     def state(self) -> np.ndarray:
         return self.env.state()
 
-    @property
-    def observation_spaces(self) -> dict[AgentID, gymnasium.spaces.Space]:
-        warnings.warn(
-            "The `observation_spaces` dictionary is deprecated. Use the `observation_space` function instead."
-        )
-        try:
-            return {
-                agent: self.observation_space(agent) for agent in self.possible_agents
-            }
-        except AttributeError as e:
-            raise AttributeError(
-                "The base environment does not have an `observation_spaces` dict attribute. Use the environments `observation_space` method instead"
-            ) from e
-
-    @property
-    def action_spaces(self) -> dict[AgentID, gymnasium.spaces.Space]:
-        warnings.warn(
-            "The `action_spaces` dictionary is deprecated. Use the `action_space` function instead."
-        )
-        try:
-            return {agent: self.action_space(agent) for agent in self.possible_agents}
-        except AttributeError as e:
-            raise AttributeError(
-                "The base environment does not have an action_spaces dict attribute. Use the environments `action_space` method instead"
-            ) from e
-
     def observation_space(self, agent: AgentID) -> gymnasium.spaces.Space:
         return self.env.observation_space(agent)
 

pettingzoo/utils/wrappers/multi_episode_env.py

@@ -59,22 +59,20 @@ def step(self, action: ActionType) -> None:
             None:
         """
         super().step(action)
-        if self.agents:
+        if self.env.agents:
             return
 
         # if we've crossed num_episodes, truncate all agents
         # and let the environment terminate normally
         if self._episodes_elapsed >= self._num_episodes:
-            self.truncations = {agent: True for agent in self.agents}
+            self.env.unwrapped.truncations = {agent: True for agent in self.env.agents}
             return
 
         # if no more agents and haven't had enough episodes,
         # increment the number of episodes and the seed for reset
         self._episodes_elapsed += 1
         self._seed = self._seed + 1 if self._seed else None
         super().reset(seed=self._seed, options=self._options)
-        self.truncations = {agent: False for agent in self.agents}
-        self.terminations = {agent: False for agent in self.agents}
 
     def __str__(self) -> str:
         """__str__.

pettingzoo/utils/wrappers/order_enforcing.py

@@ -45,7 +45,10 @@ def __getattr__(self, value: str) -> Any:
         elif value == "render_mode" and hasattr(self.env, "render_mode"):
             return self.env.render_mode  # pyright: ignore[reportGeneralTypeIssues]
         elif value == "possible_agents":
-            EnvLogger.error_possible_agents_attribute_missing("possible_agents")
+            try:
+                return self.env.possible_agents
+            except AttributeError:
+                EnvLogger.error_possible_agents_attribute_missing("possible_agents")
         elif value == "observation_spaces":
             raise AttributeError(
                 "The base environment does not have an possible_agents attribute. Use the environments `observation_space` method instead"
@@ -58,20 +61,22 @@ def __getattr__(self, value: str) -> Any:
             raise AttributeError(
                 "agent_order has been removed from the API. Please consider using agent_iter instead."
             )
-        elif value in {
-            "rewards",
-            "terminations",
-            "truncations",
-            "infos",
-            "agent_selection",
-            "num_agents",
-            "agents",
-        }:
+        elif (
+            value
+            in {
+                "rewards",
+                "terminations",
+                "truncations",
+                "infos",
+                "agent_selection",
+                "num_agents",
+                "agents",
+            }
+            and not self._has_reset
+        ):
             raise AttributeError(f"{value} cannot be accessed before reset")
         else:
-            raise AttributeError(
-                f"'{type(self).__name__}' object has no attribute '{value}'"
-            )
+            return super().__getattr__(value)
 
     def render(self) -> None | np.ndarray | str | list:
         if not self._has_reset:

pettingzoo/utils/wrappers/terminate_illegal.py

@@ -67,13 +67,13 @@ def step(self, action: ActionType) -> None:
             and not _prev_action_mask[action]
         ):
             EnvLogger.warn_on_illegal_move()
-            self._cumulative_rewards[self.agent_selection] = 0
-            self.terminations = {d: True for d in self.agents}
-            self.truncations = {d: True for d in self.agents}
+            self.env.unwrapped._cumulative_rewards[self.agent_selection] = 0
+            self.env.unwrapped.terminations = {d: True for d in self.agents}
+            self.env.unwrapped.truncations = {d: True for d in self.agents}
             self._prev_obs = None
             self._prev_info = None
-            self.rewards = {d: 0 for d in self.truncations}
-            self.rewards[current_agent] = float(self._illegal_value)
+            self.env.unwrapped.rewards = {d: 0 for d in self.truncations}
+            self.env.unwrapped.rewards[current_agent] = float(self._illegal_value)
             self._accumulate_rewards()
             self._deads_step_first()
             self._terminated = True