training_ml_control.environments.grid_world

`training_ml_control.environments.grid_world`#

Module Contents#

Classes#

`SimplifiedActions`
`SimplifiedGridEnv`
`GridWorldEnv`

Functions#

`convert_graph_to_directed`
`plot_grid_graph`
`plot_grid_all_paths_graph`	Plot all paths from start_node to target_node in shortest-path problem graph.

Data#

__all__

API#

training_ml_control.environments.grid_world.__all__#: [‘GridWorldEnv’, ‘plot_grid_graph’, ‘convert_graph_to_directed’, ‘plot_grid_all_paths_graph’]

class training_ml_control.environments.grid_world.SimplifiedActions[source]#

Bases: enum.IntEnum

right#: 0

down#: 1

left#: 2

up#: 3

class training_ml_control.environments.grid_world.SimplifiedGridEnv(mission_space: minigrid.core.mission.MissionSpace, grid_size: int | None = None, width: int | None = None, height: int | None = None, max_steps: int = 100, see_through_walls: bool = False, agent_view_size: int = 7, render_mode: str | None = None, screen_size: int | None = 640, highlight: bool = True, tile_size: int = TILE_PIXELS, agent_pov: bool = False)[source]#

Bases: minigrid.minigrid_env.MiniGridEnv

step(action: gymnasium.core.ActType) → tuple[gymnasium.core.ObsType, SupportsFloat, bool, bool, dict[str, Any]][source]#

class training_ml_control.environments.grid_world.GridWorldEnv(max_steps: int | None = None, **kwargs)[source]#

Bases: training_ml_control.environments.grid_world.SimplifiedGridEnv

static _gen_mission()[source]#

_gen_grid(width: int, height: int) → None[source]#

get_graph() → networkx.DiGraph[source]#

training_ml_control.environments.grid_world.convert_graph_to_directed(G: networkx.Graph) → networkx.DiGraph[source]#

training_ml_control.environments.grid_world.plot_grid_graph(G: networkx.Graph | networkx.DiGraph) → None[source]#

training_ml_control.environments.grid_world.plot_grid_all_paths_graph(G: networkx.Graph, *, show_solution: bool = False) → None[source]#: Plot all paths from start_node to target_node in shortest-path problem graph.