"""Functional to Environment compatibility."""
from __future__ import annotations
from typing import Any
import jax
import jax.numpy as jnp
import jax.random as jrng
import numpy as np
import gymnasium as gym
from gymnasium.envs.registration import EnvSpec
from gymnasium.functional import ActType, FuncEnv, StateType
from gymnasium.utils import seeding
from gymnasium.vector.utils import batch_space
from gymnasium.wrappers.jax_to_numpy import jax_to_numpy
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class FunctionalJaxEnv(gym.Env):
"""A conversion layer for jax-based environments."""
state: StateType
rng: jrng.PRNGKey
def __init__(
self,
func_env: FuncEnv,
metadata: dict[str, Any] | None = None,
render_mode: str | None = None,
reward_range: tuple[float, float] = (-float("inf"), float("inf")),
spec: EnvSpec | None = None,
):
"""Initialize the environment from a FuncEnv."""
if metadata is None:
metadata = {"render_mode": []}
self.func_env = func_env
self.observation_space = func_env.observation_space
self.action_space = func_env.action_space
self.metadata = metadata
self.render_mode = render_mode
self.reward_range = reward_range
self.spec = spec
self._is_box_action_space = isinstance(self.action_space, gym.spaces.Box)
if self.render_mode == "rgb_array":
self.render_state = self.func_env.render_init()
else:
self.render_state = None
np_random, _ = seeding.np_random()
seed = np_random.integers(0, 2**32 - 1, dtype="uint32")
self.rng = jrng.PRNGKey(seed)
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def reset(self, *, seed: int | None = None, options: dict | None = None):
"""Resets the environment using the seed."""
super().reset(seed=seed)
if seed is not None:
self.rng = jrng.PRNGKey(seed)
rng, self.rng = jrng.split(self.rng)
self.state = self.func_env.initial(rng=rng)
obs = self.func_env.observation(self.state)
info = self.func_env.state_info(self.state)
obs = jax_to_numpy(obs)
return obs, info
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def step(self, action: ActType):
"""Steps through the environment using the action."""
if self._is_box_action_space:
assert isinstance(self.action_space, gym.spaces.Box) # For typing
action = np.clip(action, self.action_space.low, self.action_space.high)
else: # Discrete
# For now we assume jax envs don't use complex spaces
err_msg = f"{action!r} ({type(action)}) invalid"
assert self.action_space.contains(action), err_msg
rng, self.rng = jrng.split(self.rng)
next_state = self.func_env.transition(self.state, action, rng)
observation = self.func_env.observation(next_state)
reward = self.func_env.reward(self.state, action, next_state)
terminated = self.func_env.terminal(next_state)
info = self.func_env.transition_info(self.state, action, next_state)
self.state = next_state
observation = jax_to_numpy(observation)
return observation, float(reward), bool(terminated), False, info
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def render(self):
"""Returns the render state if `render_mode` is "rgb_array"."""
if self.render_mode == "rgb_array":
self.render_state, image = self.func_env.render_image(
self.state, self.render_state
)
return image
else:
raise NotImplementedError
def close(self):
"""Closes the environments and render state if set."""
if self.render_state is not None:
self.func_env.render_close(self.render_state)
self.render_state = None
class FunctionalJaxVectorEnv(gym.vector.VectorEnv):
"""A vector env implementation for functional Jax envs."""
state: StateType
rng: jrng.PRNGKey
def __init__(
self,
func_env: FuncEnv,
num_envs: int,
max_episode_steps: int = 0,
metadata: dict[str, Any] | None = None,
render_mode: str | None = None,
reward_range: tuple[float, float] = (-float("inf"), float("inf")),
spec: EnvSpec | None = None,
):
"""Initialize the environment from a FuncEnv."""
super().__init__()
if metadata is None:
metadata = {}
self.func_env = func_env
self.num_envs = num_envs
self.single_observation_space = func_env.observation_space
self.single_action_space = func_env.action_space
self.observation_space = batch_space(
self.single_observation_space, self.num_envs
)
self.action_space = batch_space(self.single_action_space, self.num_envs)
self.metadata = metadata
self.render_mode = render_mode
self.reward_range = reward_range
self.spec = spec
self.time_limit = max_episode_steps
self.steps = jnp.zeros(self.num_envs, dtype=jnp.int32)
self.autoreset_envs = jnp.zeros(self.num_envs, dtype=jnp.bool_)
self._is_box_action_space = isinstance(self.action_space, gym.spaces.Box)
if self.render_mode == "rgb_array":
self.render_state = self.func_env.render_init()
else:
self.render_state = None
np_random, _ = seeding.np_random()
seed = np_random.integers(0, 2**32 - 1, dtype="uint32")
self.rng = jrng.PRNGKey(seed)
self.func_env.transform(jax.vmap)
def reset(self, *, seed: int | None = None, options: dict | None = None):
"""Resets the environment."""
super().reset(seed=seed)
if seed is not None:
self.rng = jrng.PRNGKey(seed)
rng, self.rng = jrng.split(self.rng)
rng = jrng.split(rng, self.num_envs)
self.state = self.func_env.initial(rng=rng)
obs = self.func_env.observation(self.state)
info = self.func_env.state_info(self.state)
self.steps = jnp.zeros(self.num_envs, dtype=jnp.int32)
obs = jax_to_numpy(obs)
return obs, info
def step(self, action: ActType):
"""Steps through the environment using the action."""
if self._is_box_action_space:
assert isinstance(self.action_space, gym.spaces.Box) # For typing
action = np.clip(action, self.action_space.low, self.action_space.high)
else: # Discrete
# For now we assume jax envs don't use complex spaces
assert self.action_space.contains(
action
), f"{action!r} ({type(action)}) invalid"
self.steps += 1
rng, self.rng = jrng.split(self.rng)
rng = jrng.split(rng, self.num_envs)
next_state = self.func_env.transition(self.state, action, rng)
reward = self.func_env.reward(self.state, action, next_state)
terminated = self.func_env.terminal(next_state)
truncated = (
self.steps >= self.time_limit
if self.time_limit > 0
else jnp.zeros_like(terminated)
)
info = self.func_env.transition_info(self.state, action, next_state)
done = jnp.logical_or(terminated, truncated)
if jnp.any(self.autoreset_envs):
to_reset = jnp.where(self.autoreset_envs)[0]
reset_count = to_reset.shape[0]
rng, self.rng = jrng.split(self.rng)
rng = jrng.split(rng, reset_count)
new_initials = self.func_env.initial(rng)
next_state = self.state.at[to_reset].set(new_initials)
self.steps = self.steps.at[to_reset].set(0)
self.autoreset_envs = done
observation = self.func_env.observation(next_state)
observation = jax_to_numpy(observation)
reward = jax_to_numpy(reward)
terminated = jax_to_numpy(terminated)
truncated = jax_to_numpy(truncated)
self.state = next_state
return observation, reward, terminated, truncated, info
def render(self):
"""Returns the render state if `render_mode` is "rgb_array"."""
if self.render_mode == "rgb_array":
self.render_state, image = self.func_env.render_image(
self.state, self.render_state
)
return image
else:
raise NotImplementedError
def close(self):
"""Closes the environments and render state if set."""
if self.render_state is not None:
self.func_env.render_close(self.render_state)
self.render_state = None
