class FourRooms(FourRoomsEnv):
""" Overwrites the original generator to make the hallway states static """
def __init__(self, agent_pos: tuple = (1, 1), goal_pos: tuple = (15, 15)):
self.hallways = {
'top' : (9, 4),
'left' : (3, 9),
'right': (16, 9),
'bot' : (9, 14)
}
super().__init__(agent_pos=agent_pos, goal_pos=goal_pos)
def _reward(self):
return 1
def _gen_grid(self, width, height):
# Create the grid
self.grid = Grid(width, height)
# Generate the surrounding walls
self.grid.horz_wall(0, 0)
self.grid.horz_wall(0, height - 1)
self.grid.vert_wall(0, 0)
self.grid.vert_wall(width - 1, 0)
room_w = width // 2
room_h = height // 2
# For each row of rooms
for j in range(0, 2):
# For each column
for i in range(0, 2):
xL = i * room_w
yT = j * room_h
xR = xL + room_w
yB = yT + room_h
# Bottom wall and door
if i + 1 < 2:
self.grid.vert_wall(xR, yT, room_h)
# pos = (xR, self._rand_int(yT + 1, yB))
# self.grid.set(*pos, None)
# Bottom wall and door
if j + 1 < 2:
self.grid.horz_wall(xL, yB, room_w)
# pos = (self._rand_int(xL + 1, xR), yB)
# self.grid.set(*pos, None)
for hallway in self.hallways.values():
self.grid.set(*hallway, None)
# Randomize the player start position and orientation
if self._agent_default_pos is not None:
self.agent_pos = self._agent_default_pos
self.grid.set(*self._agent_default_pos, None)
self.agent_dir = self._rand_int(0, 4)
else:
self.place_agent()
if self._goal_default_pos is not None:
goal = Goal()
self.grid.set(*self._goal_default_pos, goal)
goal.init_pos, goal.cur_pos = self._goal_default_pos
else:
self.place_obj(Goal())
self.mission = 'Reach the goal'
class NineRoomsEnv(MiniGridSimple):
# Only 4 actions needed, left, right, up and down
class NineRoomsCardinalActions(IntEnum):
# Cardinal movement
right = 0
down = 1
left = 2
up = 3
def __len__(self):
return 4
def __init__(
self,
grid_size=20,
passage_size=1,
max_steps=100,
seed=133,
rnd_start=0,
start_state_exclude_rooms=[],
):
self.grid_size = grid_size
self.passage_size = passage_size
self._goal_default_pos = (1, 1)
# set to 1 if agent is to be randomly spawned
self.rnd_start = rnd_start
# If self.rnd_start =1, don't spawn in these rooms
self.start_state_exclude_rooms = start_state_exclude_rooms
super().__init__(grid_size=grid_size,
max_steps=max_steps,
seed=seed,
see_through_walls=False)
self.nActions = len(NineRoomsEnv.NineRoomsCardinalActions)
# Set the action and observation spaces
self.actions = NineRoomsEnv.NineRoomsCardinalActions
self.action_space = spaces.Discrete(self.nActions)
self.max_cells = (grid_size - 1) * (grid_size - 1)
self.observation_space = spaces.Tuple(
[spaces.Discrete(grid_size),
spaces.Discrete(grid_size)])
self.observation_size = self.grid_size * self.grid_size
self.observation_shape = (self.observation_size, )
self.T = max_steps
# Change the observation space to return the position in the grid
@property
def category(self):
# [TODO] Make sure this doesn't break after self.agent_pos is changed to numpy.ndarray
return self.cell_cat_map[self.agent_pos]
def reward(self):
# -1 for every action except if the action leads to the goal state
return 1 if self.success else 0
def _gen_grid(self, width, height, val=False, seen=True):
# Create the grid
self.grid = Grid(width, height)
# Generate surrounding walls
self.grid.horz_wall(0, 0)
self.grid.horz_wall(0, height - 1)
self.grid.vert_wall(0, 0)
self.grid.vert_wall(width - 1, 0)
# Place horizontal walls through the grid
self.grid.horz_wall(0, height // 3)
self.grid.horz_wall(0, (2 * height) // 3)
# Place vertical walls through the grid
self.grid.vert_wall(width // 3, 0)
self.grid.vert_wall((2 * width) // 3, 0)
# Create passages
passage_anchors = [(width // 3, height // 3),
(width // 3, (2 * height) // 3),
((2 * width) // 3, height // 3),
((2 * width) // 3, (2 * height) // 3)]
passage_cells = []
for anchor in passage_anchors:
for delta in range(-1 * self.passage_size, self.passage_size + 1):
passage_cells.append((anchor[0] + delta, anchor[1]))
passage_cells.append((anchor[0], anchor[1] + delta))
for cell in passage_cells:
self.grid.set(*cell, None)
# Even during validation, start state distribution
# should be the same as that during training
if not self.rnd_start:
self._agent_default_pos = ((width - 2) // 2, (height - 2) // 2)
else:
self._agent_default_pos = None
# Place the agent at the center
if self._agent_default_pos is not None:
self.start_pos = self._agent_default_pos
self.grid.set(*self._agent_default_pos, None)
self.start_dir = self._rand_int(
0, 4) # Agent direction doesn't matter
else:
if len(self.start_state_exclude_rooms) == 0:
self.place_agent()
else:
valid_start_pos = []
if seen:
exclude_from = self.start_state_exclude_rooms
else:
exclude_from = [
x for x in range(1, 10)
if x not in self.start_state_exclude_rooms
]
for room in range(1, 10):
if room in exclude_from:
continue
# Ignore that there are walls for now, can handle that with rejection sampling
# Get x coordinates of allowed cells
valid_x = []
if room % 3 == 1:
valid_x = list(range(1, width // 3))
elif room % 3 == 2:
valid_x = list(range(width // 3 + 1, (2 * width) // 3))
else:
valid_x = list(range((2 * width) // 3 + 1, width - 1))
# Get valid y-coordinates of allowed cells
valid_y = []
if (room - 1) // 3 == 0:
valid_y = list(range(1, height // 3))
elif (room - 1) // 3 == 1:
valid_y = list(
range(height // 3 + 1, (2 * height) // 3))
else:
valid_y = list(range((2 * height) // 3 + 1,
height - 1))
room_cells = list(product(valid_x, valid_y))
valid_start_pos += room_cells
# Make sure start position doesn't conflict with other cells
while True:
_start_pos = valid_start_pos[np.random.choice(
len(valid_start_pos))]
row = _start_pos[1]
col = _start_pos[0]
cell = self.grid.get(row, col)
if cell is None or cell.can_overlap():
break
self.start_pos = (col, row)
self.start_dir = self._rand_int(
0, 4) # Agent direction doesn't matter
goal = Goal()
self.grid.set(*self._goal_default_pos, goal)
goal.init_pos = goal.curr_pos = self._goal_default_pos
self.mission = goal.init_pos
def reset(self, val=False, seen=True):
obs, info = super().reset(val=val, seen=seen)
# add state feature to obs
state_feat = self._encode_state(obs['agent_pos'])
obs.update(dict(state_feat=state_feat))
return obs, info
def step(self, action):
self.step_count += 1
'''
Reward doesn't depend on action, but just state.
reward = -1 if not (in_goal_state) else 0
'''
if not self.done:
# check if currently at the goal state
if self.agent_pos == self.mission:
# No penalty, episode done
self.done = True
self.success = True
else:
# Cardinal movement
if action in self.move_actions:
move_pos = self.around_pos(action)
fwd_cell = self.grid.get(*move_pos)
self.agent_dir = (action - 1) % 4
if fwd_cell == None or fwd_cell.can_overlap(
) or self.is_goal(move_pos):
self.agent_pos = move_pos
else:
raise ValueError("Invalid Action: {} ".format(action))
reward = self.reward()
if self.step_count >= self.max_steps - 1:
# print("Max Steps Exceeded.")
self.done = True
obs = self.gen_obs()
# Add state features to the observation
state_feat = self._encode_state(obs['agent_pos'])
obs.update(dict(state_feat=state_feat))
info = {
'done': self.done,
'agent_pos': np.array(self.agent_pos),
}
if self.render_rgb:
info['rgb_grid'] = self.render(mode='rgb_array')
if self.done:
info.update({
'image': self.encode_grid(),
'success': self.success,
'agent_pos': self.agent_pos,
})
return obs, reward, self.done, info
def _encode_state(self, state):
"""
Encode the state to generate observation.
"""
feat = np.ones(self.width * self.height, dtype=float)
curr_x, curr_y = state[1], state[0]
curr_pos = curr_y * self.width + curr_x
feat[curr_pos:] = 0
return feat
class FourRoomsEnv(MiniGridEnv):
"""
Classic 4 rooms gridworld environment.
Can specify agent and goal position, if not it set at random.
"""
def __init__(self, agent_pos=None, goal_pos=None, size=None):
self._agent_default_pos = agent_pos
self._goal_default_pos = goal_pos
super().__init__(grid_size=size, max_steps=math.inf) # 100)
s = CHW(3, size, size) # self.observation_space.spaces["image"]
self.observation_space = spaces.Box(
low=0,
high=255, # TODO
shape=(s.width, s.height, s.channels),
dtype='uint8')
self.states_visited = set()
def step(self, action):
obs, reward, done, infos = super().step(action)
cur_pos = (*self.agent_pos, self.agent_dir)
self.states_visited.add(cur_pos)
return self.observation(obs), reward, done, infos
def observation(self, obs):
state = obs["image"]
env = self.unwrapped
full_grid = self.grid.encode() # todo: Cache this encoding
full_grid[self.agent_pos[0]][self.agent_pos[1]] = np.array(
[OBJECT_TO_IDX['agent'], COLOR_TO_IDX['red'], self.agent_dir])
return full_grid
def reset(self):
obs = super().reset()
return self.observation(obs)
def _gen_grid(self, width, height):
# Create the grid
self.grid = Grid(width, height)
# Generate the surrounding walls
self.grid.horz_wall(0, 0)
self.grid.horz_wall(0, height - 1)
self.grid.vert_wall(0, 0)
self.grid.vert_wall(width - 1, 0)
room_w = width // 2
room_h = height // 2
# For each row of rooms
for j in range(0, 2):
# For each column
for i in range(0, 2):
xL = i * room_w
yT = j * room_h
xR = xL + room_w
yB = yT + room_h
# Bottom wall and door
if i + 1 < 2:
self.grid.vert_wall(xR, yT, room_h)
pos = (xR, self._rand_int(yT + 1, yB))
self.grid.set(*pos, None)
# Bottom wall and door
if j + 1 < 2:
self.grid.horz_wall(xL, yB, room_w)
pos = (self._rand_int(xL + 1, xR), yB)
self.grid.set(*pos, None)
# Randomize the player start position and orientation
if self._agent_default_pos is not None:
self.agent_pos = self._agent_default_pos
self.grid.set(*self._agent_default_pos, None)
# assuming random start direction
self.agent_dir = self._rand_int(0, 4)
else:
self.place_agent()
if self._goal_default_pos is not None:
goal = Goal()
self.grid.set(*self._goal_default_pos, goal)
goal.init_pos, goal.cur_pos = self._goal_default_pos
else:
self.place_obj(Goal())
self.mission = 'Reach the goal'
class Cluttered(MiniGridSimple):
# Only 4 actions needed, left, right, up and down
class ClutteredCardinalActions(IntEnum):
# Cardinal movement
right = 0
down = 1
left = 2
up = 3
def __len__(self):
return 4
def __init__(
self,
grid_size=20,
num_objects=5,
obj_size=3,
max_steps=100,
seed=133,
state_encoding="thermal",
rnd_start=0,
):
self.state_encoding = state_encoding
self.grid_size = grid_size
self.num_objects = num_objects
self.obj_size = obj_size
# set to 1 if agent is to be randomly spawned
self.rnd_start = rnd_start
self.grid_seed = 12
# This only works for 15x15 grid with 6 obstacles
#self._goal_default_pos = (6, 10)
#self._goal_default_pos = (self.grid_size-2, self.grid_size-2)
self._goal_default_pos = (7, 12)
# This is used for some of the experiments.
self._agent_default_pos = (7, 6)
# If self.rnd_start =1, don't spawn in these rooms
super().__init__(grid_size=grid_size,
max_steps=max_steps,
seed=seed,
see_through_walls=False)
self.nActions = len(Cluttered.ClutteredCardinalActions)
# Set the action and observation spaces
self.actions = Cluttered.ClutteredCardinalActions
self.action_space = spaces.Discrete(self.nActions)
self.max_cells = (grid_size - 1) * (grid_size - 1)
self.observation_space = spaces.Tuple(
[spaces.Discrete(grid_size),
spaces.Discrete(grid_size)])
self.observation_size = self.grid_size * self.grid_size
self.observation_shape = (self.observation_size, )
self.T = max_steps
# Change the observation space to return the position in the grid
def reward(self):
# -1 for every action except if the action leads to the goal state
#return 0 if self.success else -1
return 0 if self.success else -1 / self.T
def _gen_grid(self, width, height, val=False, seen=True):
assert width >= 10 and height >= 10, "Environment too small to place objects"
# Create the grid
self.grid = Grid(width, height)
# Generate surrounding walls
self.grid.horz_wall(0, 0)
self.grid.horz_wall(0, height - 1)
self.grid.vert_wall(0, 0)
self.grid.vert_wall(width - 1, 0)
np.random.seed(self.grid_seed)
for obj_idx in range(self.num_objects):
while True:
c_x, c_y = np.random.choice(list(range(
2, self.grid_size - 3))), np.random.choice(
list(range(2, self.grid_size - 3)))
#obj_size = np.random.choice(list(range(1, self.obj_size+1)))
obj_size = self.obj_size
if obj_size == 3:
cells = list(
product([c_x - 1, c_x, c_x + 1],
[c_y - 1, c_y, c_y + 1]))
elif obj_size == 2:
cells = list(product([c_x, c_x + 1], [c_y, c_y + 1]))
elif obj_size == 1:
cells = list(product([c_x], [c_y]))
else:
raise ValueError
valid = True
for cell in cells:
cell = self.grid.get(cell[0], cell[1])
if not (cell is None or cell.can_overlap()):
valid = False
break
if valid:
for cell in cells:
self.grid.set(*cell, Wall())
break
# Set the start position and the goal position depending upon where the obstacles are present
goal = Goal()
# [NOTE] : This is a hack, add option to set goal location from arguments.
self.grid.set(*self._goal_default_pos, goal)
goal.init_pos = goal.curr_pos = self._goal_default_pos
self.mission = goal.init_pos
self.start_pos = self._agent_default_pos
def reset(self, val=False, seen=True):
obs, info = super().reset(val=val, seen=seen)
# add state feature to obs
state_feat = self._encode_state(obs['agent_pos'])
obs.update(dict(state_feat=state_feat))
return obs, info
def step(self, action):
self.step_count += 1
'''
Reward doesn't depend on action, but just state.
reward = -1 if not (in_goal_state) else 0
'''
if not self.done:
# check if currently at the goal state
if self.agent_pos == self.mission:
# No penalty, episode done
self.done = True
self.success = True
else:
# Cardinal movement
if action in self.move_actions:
move_pos = self.around_pos(action)
fwd_cell = self.grid.get(*move_pos)
self.agent_dir = (action - 1) % 4
if fwd_cell == None or fwd_cell.can_overlap(
) or self.is_goal(move_pos):
self.agent_pos = move_pos
else:
raise ValueError("Invalid Action: {} ".format(action))
reward = self.reward()
if self.step_count >= self.max_steps - 1:
# print("Max Steps Exceeded.")
self.done = True
obs = self.gen_obs()
# Add state features to the observation
state_feat = self._encode_state(obs['agent_pos'])
obs.update(dict(state_feat=state_feat))
info = {
'done': self.done,
'agent_pos': np.array(self.agent_pos),
}
if self.render_rgb:
info['rgb_grid'] = self.render(mode='rgb_array')
if self.done:
info.update({
'image': self.encode_grid(),
'success': self.success,
'agent_pos': self.agent_pos,
})
return obs, reward, self.done, info
def _encode_state(self, state):
"""
Encode the state to generate observation.
"""
feat = np.ones(self.width * self.height, dtype=float)
curr_x, curr_y = state[1], state[0]
curr_pos = curr_y * self.width + curr_x
if self.state_encoding == "thermal":
feat[curr_pos:] = 0
elif self.state_encoding == "one-hot":
feat[:] = 0
feat[curr_pos] = 1
return feat
class EmptyGridWorld(MiniGridSimple):
# Only 4 actions needed, left, right, up and down
class CardnalActions(IntEnum):
# Cardinal movement
right = 0
down = 1
left = 2
up = 3
def __len__(self):
return 4
def __init__(
self,
grid_size=20,
max_steps=100,
state_encoding="thermal",
seed=133,
rnd_start=0,
):
self.state_encoding = state_encoding
self.grid_size = grid_size
self._goal_default_pos = (self.grid_size - 2, 1)
# set to 1 if agent is to be randomly spawned
self.rnd_start = rnd_start
super().__init__(grid_size=grid_size,
max_steps=max_steps,
seed=seed,
see_through_walls=False)
self.nActions = len(EmptyGridWorld.CardnalActions)
# Set the action and observation spaces
self.actions = EmptyGridWorld.CardnalActions
self.action_space = spaces.Discrete(self.nActions)
self.max_cells = (grid_size - 1) * (grid_size - 1)
self.observation_space = spaces.Tuple(
[spaces.Discrete(grid_size),
spaces.Discrete(grid_size)])
self.observation_size = self.grid_size * self.grid_size
self.observation_shape = (self.observation_size, )
self.T = max_steps
# Change the observation space to return the position in the grid
@property
def category(self):
# [TODO] Make sure this doesn't break after self.agent_pos is changed to numpy.ndarray
return self.cell_cat_map[self.agent_pos]
def reward(self):
# -1 for every action except if the action leads to the goal state
return 1 if self.success else -1 / self.T
def _gen_grid(self, width, height, val=False, seen=True):
# Create the grid
self.grid = Grid(width, height)
# Generate surrounding walls
self.grid.horz_wall(0, 0)
self.grid.horz_wall(0, height - 1)
self.grid.vert_wall(0, 0)
self.grid.vert_wall(width - 1, 0)
# Even during validation, start state distribution
# should be the same as that during training
if not self.rnd_start:
self._agent_default_pos = (1, self.grid_size - 2)
else:
self._agent_default_pos = None
# Place the agent at the center
if self._agent_default_pos is not None:
self.start_pos = self._agent_default_pos
self.grid.set(*self._agent_default_pos, None)
self.start_dir = self._rand_int(
0, 4) # Agent direction doesn't matter
goal = Goal()
self.grid.set(*self._goal_default_pos, goal)
goal.init_pos = goal.curr_pos = self._goal_default_pos
self.mission = goal.init_pos
def reset(self, val=False, seen=True):
obs, info = super().reset(val=val, seen=seen)
# add state feature to obs
state_feat = self._encode_state(obs['agent_pos'])
obs.update(dict(state_feat=state_feat))
return obs, info
def step(self, action):
self.step_count += 1
'''
Reward doesn't depend on action, but just state.
reward = -1 if not (in_goal_state) else 0
'''
if not self.done:
# check if currently at the goal state
if self.agent_pos == self.mission:
# No penalty, episode done
self.done = True
self.success = True
else:
# Cardinal movement
if action in self.move_actions:
move_pos = self.around_pos(action)
fwd_cell = self.grid.get(*move_pos)
self.agent_dir = (action - 1) % 4
if fwd_cell == None or fwd_cell.can_overlap(
) or self.is_goal(move_pos):
self.agent_pos = move_pos
else:
raise ValueError("Invalid Action: {} ".format(action))
reward = self.reward()
if self.step_count >= self.max_steps - 1:
# print("Max Steps Exceeded.")
self.done = True
obs = self.gen_obs()
# Add state features to the observation
state_feat = self._encode_state(obs['agent_pos'])
obs.update(dict(state_feat=state_feat))
info = {
'done': self.done,
'agent_pos': np.array(self.agent_pos),
}
if self.render_rgb:
info['rgb_grid'] = self.render(mode='rgb_array')
if self.done:
info.update({
'image': self.encode_grid(),
'success': self.success,
'agent_pos': self.agent_pos,
})
return obs, reward, self.done, info
def _encode_state(self, state):
"""
Encode the state to generate observation.
"""
feat = np.ones(self.width * self.height, dtype=float)
curr_x, curr_y = state[0], state[1]
curr_pos = curr_y * self.width + curr_x
if self.state_encoding == "thermal":
feat[curr_pos:] = 0
elif self.state_encoding == "one-hot":
feat[:] = 0
feat[curr_pos] = 1
return feat
