Reacher
Lerax port of Gymnasium's Reacher environment. A planar two-joint arm must move its fingertip to a random target sampled within radius 0.2 of the base each episode.
Observation space
10-dim float vector: [cos(theta1), cos(theta2), sin(theta1), sin(theta2), target_x, target_y, theta1_dot, theta2_dot, (fingertip - target)_x, (fingertip - target)_y]. Unbounded Box.
Action space
Box(low, high) from the model's actuator_ctrlrange — 2 continuous joint torques.
Reward
reward_dist + reward_ctrl where
reward_dist = -||fingertip - target|| * reward_dist_weight(default1.0)reward_ctrl = -sum(action ** 2) * reward_control_weight(default1.0)
Termination
Never terminates. No built-in truncation.
lerax.env.mujoco.Reacher
Bases: AbstractMujocoEnv[Float[Array, '...'], Float[Array, '...']]
MJX-based reacher environment matching Gymnasium's Reacher-v5.
transition
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
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. |