def __draw_base_img(self):
self._base_img = draw_grid(self._grid_shape[0], self._grid_shape[1], cell_size=CELL_SIZE, fill='white')
for row in range(self._grid_shape[0]):
for col in range(self._grid_shape[1]):
if PRE_IDS['wall'] in self._full_obs[row][col]:
fill_cell(self._base_img, (row, col), cell_size=CELL_SIZE, fill=WALL_COLOR, margin=0.05)
elif PRE_IDS['lemon'] in self._full_obs[row][col]:
fill_cell(self._base_img, (row, col), cell_size=CELL_SIZE, fill=LEMON_COLOR, margin=0.05)
elif PRE_IDS['apple'] in self._full_obs[row][col]:
fill_cell(self._base_img, (row, col), cell_size=CELL_SIZE, fill=APPLE_COLOR, margin=0.05)
def __draw_base_img(self):
self._base_img = draw_grid(self._grid_shape[0], self._grid_shape[1], cell_size=CELL_SIZE, fill='white')
for row in range(self._grid_shape[0]):
for col in range(self._grid_shape[1]):
if self.__wall_exists((row, col)):
fill_cell(self._base_img, (row, col), cell_size=CELL_SIZE, fill=WALL_COLOR)
for agent_i, pos in list(self.final_agent_pos.items())[:self.n_agents]:
row, col = pos[0], pos[1]
draw_cell_outline(self._base_img, (row, col), cell_size=CELL_SIZE, fill=AGENT_COLORS[agent_i])
def __draw_base_img(self):
# create grid and make everything black
img = draw_grid(self._grid_shape[0],
self._grid_shape[1],
cell_size=CELL_SIZE,
fill=WALL_COLOR)
# draw tracks
for i, row in enumerate(self._full_obs):
for j, col in enumerate(row):
if col == PRE_IDS['empty']:
fill_cell(img, (i, j),
cell_size=CELL_SIZE,
fill='white',
margin=0.05)
return img
def __init__(self, grid_shape: Coordinates = (5, 5), n_agents: int = 2, n_trees: int = 12,
agent_view: Tuple[int, int] = (1, 1), full_observable: bool = False,
step_cost: float = -1, tree_cutdown_reward: float = 10, max_steps: int = 100):
assert 0 < n_agents
assert n_agents + n_trees <= np.prod(grid_shape)
assert 1 <= agent_view[0] <= grid_shape[0] and 1 <= agent_view[1] <= grid_shape[1]
self._grid_shape = grid_shape
self.n_agents = n_agents
self._n_trees = n_trees
self._agent_view = agent_view
self.full_observable = full_observable
self._step_cost = step_cost
self._tree_cutdown_reward = tree_cutdown_reward
self._max_steps = max_steps
self.steps_beyond_done = 0
self.seed()
self._agents = [] # List[Agent]
# In order to speed up the environment we used the advantage of vector operations.
# Therefor we need to pad the grid size by the maximum agent_view size.
# Relative coordinates refer to the coordinates in non pad grid. These are the only
# coordinates visible to user. Extended coordinates refer to the coordinates in pad grid.
self._agent_map = None
self._tree_map = None
self._total_episode_reward = None
self._agent_dones = None
mask_size = np.prod(tuple(2 * v + 1 for v in self._agent_view))
# Agent ID (1) + Pos (2) + Step (1) + Neighborhood (2 * mask_size)
self._obs_len = (1 + 2 + 1 + 2 * mask_size)
obs_high = np.array([1.] * self._obs_len)
obs_low = np.array([0.] * self._obs_len)
if self.full_observable:
obs_high = np.tile(obs_high, self.n_agents)
obs_low = np.tile(obs_low, self.n_agents)
self.action_space = MultiAgentActionSpace([spaces.Discrete(5)] * self.n_agents)
self.observation_space = MultiAgentObservationSpace([spaces.Box(obs_low, obs_high)] * self.n_agents)
self._base_img = draw_grid(self._grid_shape[0], self._grid_shape[1], cell_size=CELL_SIZE, fill='white')
self._viewer = None
def __draw_base_img(self):
self._base_img = draw_grid(self._grid_shape[0],
self._grid_shape[1],
cell_size=CELL_SIZE,
fill='white')