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Inverted Pendulum

Lerax port of Gymnasium's Inverted Pendulum environment. A MuJoCo cart-pole: a sliding cart supports an un-actuated hinge pole. The agent applies horizontal force to the cart to keep the pole upright.

Observation space

concat(qpos, qvel) flattened — a 4-dim unbounded float vector [cart_x, pole_angle, cart_x_dot, pole_angle_dot].

Action space

Box(low, high) from the model's actuator_ctrlrange — a single continuous force applied to the cart.

Reward

+1.0 per step while healthy, 0.0 otherwise (not_terminated.astype(float)).

Termination

Terminates when state becomes non-finite or |pole_angle| > 0.2 rad. No built-in truncation.

lerax.env.mujoco.InvertedPendulum

Bases: AbstractMujocoEnv[Float[Array, '...'], Float[Array, '...']]

MJX-based inverted pendulum environment matching Gymnasium's InvertedPendulum-v5.

unwrapped property 

unwrapped: Self

Return the unwrapped environment

action_mask 

action_mask(
    state: MujocoEnvState, *, key: Key[Array, ""]
) -> None

transition 

transition(
    state: MujocoEnvState,
    action: ActType,
    *,
    key: Key[Array, ""],
) -> MujocoEnvState

truncate 

truncate(state: MujocoEnvState) -> Bool[Array, '']

state_info 

state_info(state: MujocoEnvState) -> dict

default_renderer 

default_renderer() -> MujocoRenderer

render 

render(state: MujocoEnvState, renderer: AbstractRenderer)

render_states 

render_states(
    states: Sequence[StateType],
    renderer: AbstractRenderer | Literal["auto"] = "auto",
    dt: float = 0.0,
)

Render a sequence of frames from multiple states.

Parameters:

Name Type Description Default
states Sequence[StateType]

A sequence of environment states to render.

 required
renderer AbstractRenderer | Literal['auto']

The renderer to use for rendering. If "auto", uses the default renderer.

 'auto'
dt float

The time delay between rendering each frame, in seconds.

 0.0

render_stacked 

render_stacked(
    states: StateType,
    renderer: AbstractRenderer | Literal["auto"] = "auto",
    dt: float = 0.0,
)

Render multiple frames from stacked states.

Stacked states are typically batched states stored in a pytree structure.

Parameters:

Name Type Description Default
states StateType

A pytree of stacked environment states to render.

 required
renderer AbstractRenderer | Literal['auto']

The renderer to use for rendering. If "auto", uses the default renderer.

 'auto'
dt float

The time delay between rendering each frame, in seconds.

 0.0

reset 

reset(
    *, key: Key[Array, ""]
) -> tuple[StateType, ObsType, dict]

Wrap the functional logic into a Gym API reset method.

Parameters:

Name Type Description Default
key Key[Array, '']

A JAX PRNG key for any stochasticity in the reset.

 required

Returns:

Type Description
tuple[StateType, ObsType, dict]

A tuple of the initial state, initial observation, and additional info.

step 

step(
    state: StateType,
    action: ActType,
    *,
    key: Key[Array, ""],
) -> tuple[
    StateType,
    ObsType,
    Float[Array, ""],
    Bool[Array, ""],
    Bool[Array, ""],
    dict,
]

Wrap the functional logic into a Gym API step method.

Parameters:

Name Type Description Default
state StateType

The current environment state.

 required
action ActType

The action to take.

 required
key Key[Array, '']

A JAX PRNG key for any stochasticity in the step.

 required

Returns:

Type Description
tuple[StateType, ObsType, Float[Array, ''], Bool[Array, ''], Bool[Array, ''], dict]

A tuple of the next state, observation, reward, terminal flag, truncate flag, and additional info.

model_from_path staticmethod 

model_from_path(xml_file: str | Path) -> mujoco.MjModel