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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""Generic Gymnasium environment server implementation."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Optional
import numpy as np
try:
import gymnasium as gym
from gymnasium import spaces
except ImportError:
raise ValueError("Please install gymnasium with: pip install gymnasium")
from core.env_server import Environment
from ..models import GymAction, GymObservation, GymState
logger = logging.getLogger(__name__)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
class GymnasiumEnvironment(Environment):
"""
Generic Gymnasium environment wrapper for OpenEnv.
Any Gymnasium environment can be served by providing its environment id.
The wrapper handles common concerns such as seed management, type conversion,
and JSON-friendly serialization of observations.
"""
def __init__(
self,
env_id: str,
render_mode: Optional[str] = None,
max_steps: Optional[int] = None,
seed: Optional[int] = None,
**gym_kwargs,
):
super().__init__()
self.env_id = env_id
self.render_mode = render_mode
self.max_steps = max_steps if max_steps and max_steps > 0 else None
self._initial_seed = seed
self._next_seed = seed
logger.info(
"Creating Gymnasium environment '%s' (render_mode=%s, max_steps=%s, seed=%s)",
env_id,
render_mode,
self.max_steps,
seed,
)
self.env = gym.make(env_id, render_mode=render_mode, **gym_kwargs)
if self.max_steps is not None:
self.env = gym.wrappers.TimeLimit(
self.env, max_episode_steps=self.max_steps
)
self._action_space_metadata = self._describe_space(self.env.action_space)
self._observation_space_metadata = self._describe_space(
self.env.observation_space
)
self._legal_actions = self._summarize_action_space(self.env.action_space)
self._state = GymState(
env_id=env_id,
render_mode=render_mode,
max_steps=self.max_steps,
seed=seed,
)
logger.info("GymnasiumEnvironment for '%s' initialized", env_id)
def reset(self) -> GymObservation:
"""Reset the environment and return the initial observation."""
seed = self._consume_seed()
obs, info = self.env.reset(seed=seed)
self._state.episode_id = str(uuid.uuid4())
self._state.step_count = 0
self._state.episode_length = 0
self._state.total_reward = 0.0
self._state.seed = seed
observation = self._make_observation(
obs=obs,
reward=None,
done=False,
info=info,
terminated=False,
truncated=False,
raw_reward=0.0,
)
logger.info(
"Environment '%s' reset (episode_id=%s, seed=%s)",
self.env_id,
self._state.episode_id,
seed,
)
return observation
def step(self, action: GymAction) -> GymObservation:
"""Execute an action and return the resulting observation."""
gym_action = self._convert_action(action)
obs, reward, terminated, truncated, info = self.env.step(gym_action)
self._state.step_count += 1
self._state.episode_length += 1
reward_value, raw_reward = self._normalize_reward(reward)
if reward_value is not None:
self._state.total_reward += reward_value
done = bool(terminated or truncated)
observation = self._make_observation(
obs=obs,
reward=reward_value,
done=done,
info=info,
terminated=terminated,
truncated=truncated,
raw_reward=raw_reward,
)
logger.debug(
"Step %s -> reward=%s terminated=%s truncated=%s",
self._state.step_count,
reward,
terminated,
truncated,
)
return observation
@property
def state(self) -> GymState:
"""Return the current environment state."""
return self._state
def close(self) -> None:
"""Close the underlying Gymnasium environment."""
logger.info("Closing GymnasiumEnvironment for '%s'", self.env_id)
if hasattr(self.env, "close"):
self.env.close()
logger.info("GymnasiumEnvironment closed")
# ------------------------------------------------------------------
# Internal helpers
# ------------------------------------------------------------------
def _consume_seed(self) -> Optional[int]:
if self._next_seed is None:
return None
seed = self._next_seed
self._next_seed += 1
return seed
def _convert_action(self, action: GymAction) -> Any:
if not isinstance(action, GymAction):
raise ValueError(f"Expected GymAction, received {type(action)}")
raw_action = action.action
space = self.env.action_space
converted = self._convert_action_for_space(space, raw_action)
if not space.contains(converted):
raise ValueError(
f"Action {raw_action!r} could not be converted for space {space}"
)
return converted
def _convert_action_for_space(self, space: spaces.Space, value: Any) -> Any:
if isinstance(space, spaces.Discrete):
return int(value)
if isinstance(space, spaces.MultiDiscrete):
return np.asarray(value, dtype=space.dtype)
if isinstance(space, spaces.MultiBinary):
return np.asarray(value, dtype=space.dtype)
if isinstance(space, spaces.Box):
return np.asarray(value, dtype=space.dtype)
if isinstance(space, spaces.Tuple):
if not isinstance(value, (list, tuple)):
raise TypeError(
f"Tuple action space expects list/tuple, received {type(value)}"
)
if len(value) != len(space.spaces):
raise ValueError(
f"Tuple action with length {len(value)} does not match "
f"expected length {len(space.spaces)}"
)
return tuple(
self._convert_action_for_space(subspace, subvalue)
for subspace, subvalue in zip(space.spaces, value)
)
if isinstance(space, spaces.Dict):
if not isinstance(value, dict):
raise TypeError(
f"Dict action space expects dict, received {type(value)}"
)
return {
key: self._convert_action_for_space(space.spaces[key], value[key])
for key in space.spaces
}
if isinstance(space, spaces.Text):
return str(value)
return value
def _normalize_reward(self, reward: Any) -> tuple[Optional[float], Any]:
if isinstance(reward, (int, float)):
value = float(reward)
return value, value
if isinstance(reward, (np.integer, np.floating)):
value = float(reward.item())
return value, value
return None, self._to_serializable(reward)
def _make_observation(
self,
obs: Any,
reward: Optional[float],
done: bool,
info: Dict[str, Any],
terminated: bool,
truncated: bool,
raw_reward: Any,
) -> GymObservation:
metadata = {
"env_id": self.env_id,
"render_mode": self.render_mode,
"max_steps": self.max_steps,
"seed": self._state.seed,
"info": self._to_serializable(info),
"raw_reward": raw_reward,
"terminated": terminated,
"truncated": truncated,
"action_space": self._action_space_metadata,
"observation_space": self._observation_space_metadata,
}
# Remove keys with None values for cleaner payloads
metadata = {key: value for key, value in metadata.items() if value is not None}
return GymObservation(
state=self._to_serializable(obs),
legal_actions=self._legal_actions,
episode_length=self._state.episode_length,
total_reward=self._state.total_reward,
done=done,
reward=reward,
metadata=metadata,
)
def _describe_space(self, space: spaces.Space) -> Dict[str, Any]:
description: Dict[str, Any] = {"type": type(space).__name__}
if hasattr(space, "shape"):
description["shape"] = self._to_serializable(getattr(space, "shape"))
dtype = getattr(space, "dtype", None)
if dtype is not None:
description["dtype"] = str(dtype)
if isinstance(space, spaces.Discrete):
description["n"] = int(space.n)
elif isinstance(space, spaces.MultiDiscrete):
description["nvec"] = self._to_serializable(space.nvec)
elif isinstance(space, spaces.MultiBinary):
description["n"] = self._to_serializable(space.n)
elif isinstance(space, spaces.Box):
description["low"] = self._to_serializable(space.low)
description["high"] = self._to_serializable(space.high)
elif isinstance(space, spaces.Tuple):
description["spaces"] = [
self._describe_space(subspace) for subspace in space.spaces
]
elif isinstance(space, spaces.Dict):
description["spaces"] = {
key: self._describe_space(subspace)
for key, subspace in space.spaces.items()
}
elif isinstance(space, spaces.Text):
description["min_length"] = space.min_length
description["max_length"] = space.max_length
return description
def _summarize_action_space(self, space: spaces.Space) -> Any:
if isinstance(space, spaces.Discrete):
return list(range(int(space.n)))
if isinstance(space, spaces.MultiDiscrete):
return [list(range(int(n))) for n in self._to_serializable(space.nvec)]
if isinstance(space, spaces.MultiBinary):
return [0, 1]
if isinstance(space, spaces.Box):
return {
"low": self._to_serializable(space.low),
"high": self._to_serializable(space.high),
}
if isinstance(space, spaces.Tuple):
return [self._summarize_action_space(subspace) for subspace in space.spaces]
if isinstance(space, spaces.Dict):
return {
key: self._summarize_action_space(subspace)
for key, subspace in space.spaces.items()
}
if isinstance(space, spaces.Text):
return {"charset": "unicode"}
return None
def _to_serializable(self, value: Any) -> Any:
if isinstance(value, np.ndarray):
return [self._to_serializable(v) for v in value.tolist()]
if isinstance(value, (np.floating, np.integer)):
return self._to_serializable(value.item())
if isinstance(value, np.bool_):
return bool(value)
if isinstance(value, (list, tuple, set)):
return [self._to_serializable(v) for v in value]
if isinstance(value, dict):
return {str(k): self._to_serializable(v) for k, v in value.items()}
if isinstance(value, (int, bool, float)) or value is None:
return value
return str(value)
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