hyphi_gym.envs.FlatGrid#

Package Contents#

Classes#

Grid

Base for grid-based games managing

Board

Base for grid-based games managing

FlatGrid

Base for grid-based games managing
class hyphi_gym.envs.FlatGrid.Grid(render_mode: hyphi_gym.common.board.Optional[str] = None, **simargs)#

Bases: hyphi_gym.common.rendering.Rendering, hyphi_gym.common.point.Point

Base for grid-based games managing • A layout of a variable size • Containing CELLS ∈ [WALL, FIELD, AGENT, TARGET, HOLE] • Navigable with ACTIONS ∈ [UP, RIGHT, DOWN,LEFT]

step_scale = 1#
base_xml#
metadata#
render() → hyphi_gym.common.board.Optional[hyphi_gym.common.board.np.ndarray]#

Return rendering of current state as np array if render_mode set

reset(**kwargs) → tuple[hyphi_gym.common.board.np.ndarray, dict]#

Gymnasium compliant function to reset the environment

_distance()#
execute(action: int) → tuple[hyphi_gym.common.board.np.ndarray, dict]#

Helper function to step the environment, executing action, returning its consequences

class hyphi_gym.envs.FlatGrid.Board(size: tuple[int, int], layout: list[str] | None, bound=None, **kwargs)#

Bases: hyphi_gym.common.base.Base

Base for grid-based games managing • A layout of a variable size • Containing CELLS ∈ [WALL, FIELD, AGENT, TARGET, HOLE] • Navigable with ACTIONS ∈ [UP, RIGHT, DOWN,LEFT]

board: numpy.ndarray#
size: tuple[int, int]#
layout: numpy.ndarray | None#
ascii(grid: numpy.ndarray | None = None) → list[str]#

Transform 2D-INT Array to list of strings

_grid(ascii: list[str]) → numpy.ndarray#

Transform 1D string to 2D-INT Array

getpos(board: numpy.ndarray | None = None, cell: str = AGENT) → numpy.ndarray#

Position helper for finding the vector position of cell on the board or internal board

newpos(position: numpy.ndarray | tuple[int, int], action: int, n=1) → tuple[int, int]#

Action helper mutating a position tuple by appying action n-times

iterate_actions(p: tuple[int, int], n=1, condition=lambda act, pos, n: ...) → dict[int, tuple[int, int]]#

Return possible n actions in a bounded box given a position p and their mutated positions

action_possible(act: int, pos: tuple[int, int], n=1) → bool#

Return possible n actions act in a bounded box given a position pos

_generate()#

Random generator for flat grids, creating a grid of size

_validate(board, error=True, setup=False)#

Overwrite this function to validate layout return steps to solution

_update(key: str, oldpos, newpos)#
_randomize(board: numpy.ndarray, key: str)#

Mutation function to randomize the position of cell on board

reset(**kwargs) → tuple[gymnasium.spaces.Space, dict]#

Gymnasium compliant function to reset the environment

class hyphi_gym.envs.FlatGrid.FlatGrid(size, random=[], render_mode=None, **kwargs)#

Bases: hyphi_gym.common.grid.Grid, hyphi_gym.common.board.Board

Base for grid-based games managing • A layout of a variable size • Containing CELLS ∈ [WALL, FIELD, AGENT, TARGET, HOLE] • Navigable with ACTIONS ∈ [UP, RIGHT, DOWN,LEFT]