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Acrobot

lerax.env.classic_control.Acrobot

Bases: AbstractClassicControlEnv[AcrobotState, Int[Array, ''], Float[Array, '4']]

Acrobot environment matching the Gymnasium Acrobot environment.

Note

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

Action Space

The action space is discrete with three actions:

  • 0: Apply -1 torque to the joint between the two links
  • 1: Apply 0 torque
  • 2: Apply +1 torque to the joint between the two links

The action applies a fixed magnitude torque to the joint for the duration of the time step.

Observation Space

The observation space is a 6-dimensional continuous space representing the state of the two links:

Index Observation Min Value Max Value
0 Cosine of Joint Angle 1 -1.0 1.0
1 Sine of Joint Angle 1 -1.0 1.0
2 Cosine of Joint Angle 2 -1.0 1.0
3 Sine of Joint Angle 2 -1.0 1.0
4 Joint Velocity 1 -4π 4π
5 Joint Velocity 2 -9π 9π

Reward

Non-terminal steps yield a reward of -1.0 and the terminal step yields a reward of 0.0.

Termination

The episode terminates when the tip of the second link reaches a height greater than 1.0 unit above the base. This corresponds to the condition: -cos(theta1) - cos(theta1 + theta2) > 1.0

Parameters:

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

Gravitational acceleration.

 9.8
link_length_1 Float[ArrayLike, '']

Length of the first link.

 1.0
link_length_2 Float[ArrayLike, '']

Length of the second link.

 1.0
link_mass_1 Float[ArrayLike, '']

Mass of the first link.

 1.0
link_mass_2 Float[ArrayLike, '']

Mass of the second link.

 1.0
link_com_pos_1 Float[ArrayLike, '']

Center of mass position of the first link.

 0.5
link_com_pos_2 Float[ArrayLike, '']

Center of mass position of the second link.

 0.5
link_moi Float[ArrayLike, '']

Moment of inertia of the links.

 1.0
max_vel_1 Float[ArrayLike, '']

Maximum angular velocity for the first joint.

 4 * jnp.pi
max_vel_2 Float[ArrayLike, '']

Maximum angular velocity for the second joint.

 9 * jnp.pi
torque_max_noise Float[ArrayLike, '']

Maximum noise to add to the applied torque.

 0.0
torques Float[ArrayLike, '3']

Array of possible torques corresponding to each action.

 jnp.array([-1.0, 0.0, 1.0])
dt Float[ArrayLike, '']

Time step for integration.

 0.2
solver diffrax.AbstractSolver | None

Diffrax solver to use for integration.

 None
stepsize_controller diffrax.AbstractStepSizeController | None

Step size controller for adaptive solvers.

 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.