"""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 gymnasium as gym
from gymnasium.envs.registration import EnvSpec
from gymnasium.experimental.functional import ActType, FuncEnv, StateType
from gymnasium.utils import seeding
from gymnasium.vector.utils import batch_space
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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,
spec: EnvSpec | None = None,
):
"""Initialize the environment from a FuncEnv."""
if metadata is None:
# metadata.get("jax", False) can be used downstream to know that the environment returns jax arrays
metadata = {"render_mode": [], "jax": True}
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.spec = spec
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, rng)
info = self.func_env.state_info(self.state)
return obs, info
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def step(self, action: ActType):
"""Steps through the environment using the action."""
rng, self.rng = jrng.split(self.rng)
next_state = self.func_env.transition(self.state, action, rng)
observation = self.func_env.observation(next_state, rng)
reward = self.func_env.reward(self.state, action, next_state, rng)
terminated = self.func_env.terminal(next_state, rng)
info = self.func_env.transition_info(self.state, action, next_state)
self.state = next_state
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,
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.spec = spec
self.time_limit = max_episode_steps
self.steps = jnp.zeros(self.num_envs, dtype=jnp.int32)
self.prev_done = jnp.zeros(self.num_envs, dtype=jnp.bool_)
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, rng)
info = self.func_env.state_info(self.state)
self.steps = jnp.zeros(self.num_envs, dtype=jnp.int32)
return obs, info
def step(self, action: ActType):
"""Steps through the environment using the action."""
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, rng)
terminated = self.func_env.terminal(next_state, rng)
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)
if jnp.any(self.prev_done):
to_reset = jnp.where(self.prev_done)[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)
terminated = terminated.at[to_reset].set(False)
truncated = truncated.at[to_reset].set(False)
self.prev_done = jnp.logical_or(terminated, truncated)
rng = jrng.split(self.rng, self.num_envs)
observation = self.func_env.observation(next_state, rng)
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
