Maze

Maze (Environment)

A JAX implementation of a 2D Maze. The goal is to navigate the maze to find the target position.

• observation:

  • agent_position: current 2D Position of agent.
  • target_position: 2D Position of target cell.
  • walls: jax array (bool) of shape (num_rows, num_cols) whose values are True where walls are and False for empty cells.
  • action_mask: array (bool) of shape (4,) defining the available actions in the current position.
  • step_count: jax array (int32) of shape () step number of the episode.

• action: jax array (int32) of shape () specifying which action to take: [0,1,2,3] correspond to [Up, Right, Down, Left]. If an invalid action is taken, i.e. there is a wall blocking the action, then no action (no-op) is taken.

• reward: jax array (float32) of shape (): 1 if the target is reached, 0 otherwise.

• episode termination (if any):

  • agent reaches the target position.
  • the time_limit is reached.

• state: State:

  • agent_position: current 2D Position of agent.
  • target_position: 2D Position of target cell.
  • walls: jax array (bool) of shape (num_rows, num_cols) whose values are True where walls are and False for empty cells.
  • action_mask: array (bool) of shape (4,) defining the available actions in the current position.
  • step_count: jax array (int32) of shape () step number of the episode.
  • key: random key (uint) of shape (2,).

from jumanji.environments import Maze
env = Maze()
key = jax.random.PRNGKey(0)
state, timestep = jax.jit(env.reset)(key)
env.render(state)
action = env.action_spec.generate_value()
state, timestep = jax.jit(env.step)(state, action)
env.render(state)

observation_spec: jumanji.specs.Spec[jumanji.environments.routing.maze.types.Observation] cached property writable

Specifications of the observation of the Maze environment.

Returns:

Type	Description
Spec for the `Observation` whose fields are	agent_position: tree of BoundedArray (int32) of shape (). target_position: tree of BoundedArray (int32) of shape (). walls: BoundedArray (bool) of shape (num_rows, num_cols). step_count: Array (int32) of shape (). action_mask: BoundedArray (bool) of shape (4,).

action_spec: DiscreteArray cached property writable

Returns the action spec. 4 actions: [0,1,2,3] -> [Up, Right, Down, Left].

Returns:

Type	Description
action_spec	discrete action space with 4 values.

__init__(self, generator: Optional[jumanji.environments.routing.maze.generator.Generator] = None, time_limit: Optional[int] = None, viewer: Optional[jumanji.viewer.Viewer[jumanji.environments.routing.maze.types.State]] = None) -> None special

Instantiates a Maze environment.

Parameters:

Name	Type	Description	Default
generator	Optional[jumanji.environments.routing.maze.generator.Generator]	Generator whose __call__ instantiates an environment instance. Implemented options are [ToyGenerator, RandomGenerator]. Defaults to RandomGenerator with num_rows=10 and num_cols=10.	None
time_limit	Optional[int]	the time_limit of an episode, i.e. the maximum number of environment steps before the episode terminates. By default, time_limit = num_rows * num_cols.	None
viewer	Optional[jumanji.viewer.Viewer[jumanji.environments.routing.maze.types.State]]	Viewer used for rendering. Defaults to MazeEnvViewer with "human" render mode.	None

reset(self, key: PRNGKeyArray) -> Tuple[jumanji.environments.routing.maze.types.State, jumanji.types.TimeStep[jumanji.environments.routing.maze.types.Observation]]

Resets the environment by calling the instance generator for a new instance.

Parameters:

Name	Type	Description	Default
key	PRNGKeyArray	random key used to reset the environment since it is stochastic.	required

Returns:

Type	Description
state	State object corresponding to the new state of the environment after a reset. timestep: TimeStep object corresponding the first timestep returned by the environment after a reset.

step(self, state: State, action: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]) -> Tuple[jumanji.environments.routing.maze.types.State, jumanji.types.TimeStep[jumanji.environments.routing.maze.types.Observation]]

Run one timestep of the environment's dynamics.

If an action is invalid, the agent does not move, i.e. the episode does not automatically terminate.

Parameters:

Name	Type	Description	Default
state	State	State object containing the dynamics of the environment.	required
action	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number]	(int32) specifying which action to take: [0,1,2,3] correspond to [Up, Right, Down, Left]. If an invalid action is taken, i.e. there is a wall blocking the action, then no action (no-op) is taken.	required

Returns:

Type	Description
state	the next state of the environment. timestep: the next timestep to be observed.

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Last update: 2024-03-29