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OpenEnv - Minimal RL Environment Framework
A lightweight replacement for Gymnasium that provides the core RL environment
interface needed for OpenEnv compliance.
This module provides:
- Base Env class with reset/step interface
- Space classes for action/observation definitions
- Basic seeding and rendering support
"""
from abc import ABC, abstractmethod
from typing import Any, Dict as DictType, Optional, Tuple, Union
import numpy as np
# Import the existing OpenEnv Env class
try:
from openenv.env import Env as BaseEnv
except ImportError:
# Fallback if not available
BaseEnv = object
class Space(ABC):
"""Abstract base class for observation and action spaces."""
@abstractmethod
def sample(self) -> Any:
"""Sample a random element from this space."""
pass
@abstractmethod
def contains(self, x: Any) -> bool:
"""Check if x is contained in this space."""
pass
def __repr__(self) -> str:
return f"{self.__class__.__name__}()"
class Discrete(Space):
"""A discrete space with n possible values (0 to n-1)."""
def __init__(self, n: int):
self.n = n
def sample(self) -> int:
return np.random.randint(self.n)
def contains(self, x: Any) -> bool:
return isinstance(x, (int, np.integer)) and 0 <= x < self.n
def __repr__(self) -> str:
return f"Discrete({self.n})"
class Box(Space):
"""A continuous space with bounds."""
def __init__(self, low: Union[float, np.ndarray], high: Union[float, np.ndarray],
shape: Optional[Tuple[int, ...]] = None, dtype: Any = np.float32):
self.low = np.array(low, dtype=dtype)
self.high = np.array(high, dtype=dtype)
self.shape = shape or self.low.shape
self.dtype = dtype
def sample(self) -> np.ndarray:
return np.random.uniform(self.low, self.high, self.shape).astype(self.dtype)
def contains(self, x: Any) -> bool:
return (np.all(x >= self.low) and np.all(x <= self.high) and
x.shape == self.shape and x.dtype == self.dtype)
def __repr__(self) -> str:
return f"Box({self.low}, {self.high}, {self.shape}, {self.dtype})"
class Dict(Space):
"""A dictionary of spaces."""
def __init__(self, spaces: DictType[str, Space]):
self.spaces = spaces
def sample(self) -> DictType[str, Any]:
return {key: space.sample() for key, space in self.spaces.items()}
def contains(self, x: Any) -> bool:
return (isinstance(x, dict) and
all(key in self.spaces and self.spaces[key].contains(value)
for key, value in x.items()))
def __repr__(self) -> str:
return f"Dict({self.spaces})"
class Env(BaseEnv):
"""
OpenEnv environment base class compatible with Gymnasium interface.
Provides the standard RL environment interface compatible with OpenEnv.
"""
def __init__(self, name: str = "OpenEnv", state_space=None, action_space=None, episode_max_length: int = 1000):
if BaseEnv is not object:
super().__init__(name, state_space, action_space, episode_max_length)
self.metadata = {}
self.render_mode = None
self.np_random = np.random.RandomState()
def reset(self, *, seed: Optional[int] = None, options: Optional[DictType[str, Any]] = None) -> Any:
"""
Reset the environment to initial state.
Args:
seed: Random seed for reproducibility
options: Additional reset options
Returns:
Initial observation
"""
if seed is not None:
self.np_random.seed(seed)
return self._reset(seed=seed, options=options)
def _reset(self, *, seed: Optional[int] = None, options: Optional[DictType[str, Any]] = None) -> Any:
"""Internal reset implementation."""
raise NotImplementedError("Subclasses must implement _reset")
def step(self, action: Any) -> Tuple[Any, Any, bool, bool, DictType[str, Any]]:
"""
Take an action in the environment.
Args:
action: Action to take
Returns:
observation, reward, terminated, truncated, info
"""
return self._step(action)
def _step(self, action: Any) -> Tuple[Any, Any, bool, bool, DictType[str, Any]]:
"""Internal step implementation."""
raise NotImplementedError("Subclasses must implement _step")
def render(self) -> Any:
"""Render the environment."""
return None
def close(self) -> None:
"""Clean up environment resources."""
pass
def seed(self, seed: Optional[int] = None) -> None:
"""Set random seed."""
if seed is not None:
self.np_random.seed(seed)
@property
def unwrapped(self) -> 'Env':
"""Return the unwrapped environment."""
return self
# Create the spaces module
class _Spaces:
"""Container for space classes."""
Discrete = Discrete
Box = Box
Dict = Dict
spaces = _Spaces() |