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Continuous Mountain Car

lerax.env.classic_control.ContinuousMountainCar

Bases: AbstractClassicControlEnv[ContinuousMountainCarState, Float[Array, ''], Float[Array, '2']]

Continuous Mountain Car environment matching the Gymnasium Continuous MountainCar environment.

Note

To achieve identical dynamics to Gymnasium set solver=diffrax.Euler().

Action Space

The action space is a 1-dimensional continuous space representing the force applied to the car in the range [-1.0, 1.0].

Observation Space

The observation space is a 2-dimensional continuous space representing the position and velocity of the car:

Index Observation Min Value Max Value
0 Car Position -1.2 0.6
1 Car Velocity -0.07 0.07

Parameters:

Name Type Description Default
min_action Float[ArrayLike, '']

Minimum action value (default: -1.0).

 -1.0
max_action Float[ArrayLike, '']

Maximum action value (default: 1.0).

 1.0
min_position Float[ArrayLike, '']

Minimum position of the car (default: -1.2).

 -1.2
max_position Float[ArrayLike, '']

Maximum position of the car (default: 0.6).

 0.6
max_speed Float[ArrayLike, '']

Maximum speed of the car (default: 0.07).

 0.07
goal_position Float[ArrayLike, '']

Position at which the goal is reached (default: 0.5).

 0.5
power Float[ArrayLike, '']

Power of the car's engine (default: 0.0015).

 0.0015
dt Float[ArrayLike, '']

Time step for each action (default: 1.0).

 1.0
solver diffrax.AbstractSolver | None

Diffrax solver to use for ODE integration (default: Tsit5).

 None
stepsize_controller diffrax.AbstractStepSizeController | None

Step size controller for adaptive solvers (default: PIDController with rtol=1e-5, atol=1e-5).

 None

unwrapped property 

unwrapped: Self

Return the unwrapped environment

action_mask 

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

transition 

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

truncate 

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

state_info 

state_info(state: StateType) -> dict

transition_info 

transition_info(
    state: StateType, action: ActType, next_state: StateType
) -> dict

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.