def __init__(self,
mdp,
lower_values_init,
upper_values_init,
tau=10.,
name='BRTDP'):
'''
Args:
mdp (MDP): underlying MDP to plan in
lower_values_init (defaultdict): lower bound initialization on the value function
upper_values_init (defaultdict): upper bound initialization on the value function
tau (float): scaling factor to help determine when the bounds on the value function are tight enough
name (str): Name of the planner
'''
Planner.__init__(self, mdp, name)
self.lower_values = lower_values_init
self.upper_values = upper_values_init
# Using the value iteration class for accessing the matrix of transition probabilities
vi = ValueIteration(mdp, sample_rate=1000)
self.states = vi.get_states()
vi._compute_matrix_from_trans_func()
self.trans_dict = vi.trans_dict
self.max_diff = (self.upper_values[self.mdp.init_state] -
self.lower_values[self.mdp.init_state]) / tau
def get_transition_matrix(mdp):
'''
Args:
mdp
Returns:
T (list): transition matrix
state_to_id (dict)
id_to_state (dict)
'''
vi = ValueIteration(mdp) # Use VI class to enumerate states
vi.run_vi()
vi._compute_matrix_from_trans_func()
# q = vi.get_q_function()
trans_matrix = vi.trans_dict
state_to_id = {}
id_to_state = {}
for i, u in enumerate(trans_matrix):
state_to_id[u] = i
id_to_state[i] = u
T = np.zeros((len(trans_matrix), len(trans_matrix)), dtype=np.int8)
for i, u in enumerate(trans_matrix):
for j, a in enumerate(trans_matrix[u]):
for k, v in enumerate(trans_matrix[u][a]):
if trans_matrix[u][a][v] > 0:
T[i][state_to_id[v]] = 1 # Node index starts from 1 (Minizinc is 1-indexed language)
return T, state_to_id, id_to_state
def __init__(self, mdp, lower_values_init, upper_values_init, tau=10., name='BRTDP'):
'''
Args:
mdp (MDP): underlying MDP to plan in
lower_values_init (defaultdict): lower bound initialization on the value function
upper_values_init (defaultdict): upper bound initialization on the value function
tau (float): scaling factor to help determine when the bounds on the value function are tight enough
name (str): Name of the planner
'''
Planner.__init__(self, mdp, name)
self.lower_values = lower_values_init
self.upper_values = upper_values_init
# Using the value iteration class for accessing the matrix of transition probabilities
vi = ValueIteration(mdp, sample_rate=1000)
self.states = vi.get_states()
vi._compute_matrix_from_trans_func()
self.trans_dict = vi.trans_dict
self.max_diff = (self.upper_values[self.mdp.init_state] - self.lower_values[self.mdp.init_state]) / tau
class MonotoneLowerBound(Planner):
def __init__(self, mdp, name='MonotoneUpperBound'):
relaxed_mdp = MonotoneLowerBound._construct_deterministic_relaxation_mdp(mdp)
Planner.__init__(self, relaxed_mdp, name)
self.vi = ValueIteration(relaxed_mdp)
self.states = self.vi.get_states()
self.vi._compute_matrix_from_trans_func()
self.vi.run_vi()
self.lower_values = self._construct_lower_values()
@staticmethod
def _construct_deterministic_relaxation_mdp(mdp):
relaxed_mdp = copy.deepcopy(mdp)
relaxed_mdp.set_slip_prob(0.0)
return relaxed_mdp
def _construct_lower_values(self):
values = defaultdict()
for state in self.states:
values[state] = self.vi.get_value(state)
return values
class MonotoneLowerBound(Planner):
def __init__(self, mdp, name='MonotoneUpperBound'):
relaxed_mdp = MonotoneLowerBound._construct_deterministic_relaxation_mdp(
mdp)
Planner.__init__(self, relaxed_mdp, name)
self.vi = ValueIteration(relaxed_mdp)
self.states = self.vi.get_states()
self.vi._compute_matrix_from_trans_func()
self.vi.run_vi()
self.lower_values = self._construct_lower_values()
@staticmethod
def _construct_deterministic_relaxation_mdp(mdp):
relaxed_mdp = copy.deepcopy(mdp)
relaxed_mdp.set_slip_prob(0.0)
return relaxed_mdp
def _construct_lower_values(self):
values = defaultdict()
for state in self.states:
values[state] = self.vi.get_value(state)
return values
class NavigationWorldMDP(MDP):
"""Class for Navigation MDP from:
MacGlashan, James, and Michael L. Littman. "Between Imitation and
Intention Learning." IJCAI. 2015.
"""
# Static constants.
ACTIONS = ["up", "down", "left", "right"]
CELL_KIND_NAV = "nav"
CELL_KIND_WALL = "wall"
CELL_KIND_GOAL = "goal"
def __init__(self,
width=30,
height=30,
nav_cell_types=["white", "yellow", "red", "green", "purple"],
nav_cell_rewards=[0, 0, -10, -10, -10],
nav_cell_p_or_locs=[0.68, 0.17, 0.05, 0.05, 0.05],
wall_cell_types=[],
wall_cell_rewards=[],
wall_cell_locs=[],
goal_cell_types=["blue"],
goal_cell_rewards=[1.],
goal_cell_locs=[[(21, 21)]],
init_loc=(1, 1),
rand_init=False,
gamma=0.99, slip_prob=0.00, step_cost=0.5,
is_goal_terminal=True,
name="Navigation MDP"):
"""Navigation World MDP constructor.
Args:
height (int): Navigation grid height (in no. of cells).
width (int): Navigation grid width (in no. of cells).
nav_cell_types (list of <str / int>): Navigation cell types.
nav_cell_rewards (list of float): Rewards associated with
@nav_cell_types.
nav_cell_p_or_locs (list of <float /
list of tuples (int x, int y)>):
Probability corresponding to @nav_cell_types distribution, or
it could be list of fixed/forced locations [(x,y),...].
(Default values are chosen arbitrarily larger than percolation
threshold for square lattice--just an approximation to match
cell distribution in the paper.)
goal_cell_types (list of <str / int>): Goal cell types.
goal_cell_locs (list of list of tuples (int, int)): Goal cell
locations [(x,y),...] associated with @goal_cell_types.
nav_cell_rewards (list of float): Rewards associated with
@goal_cell_types.
init_loc (int x, int y): Init cell to compute state space
reachability and value iteration.
gamma (float): MDP discount factor
slip_prob (float): With this probability agent could fall either
on left or right from the intended cell.
step_cost (float): Living penalty.
is_goal_terminal (bool): True to set goal state terminal.
Note:
Locations are specified in (x,y) format, but (row, col)
convention is used while storing in memory.
"""
assert len(nav_cell_types) == len(nav_cell_rewards) \
== len(nav_cell_p_or_locs)
assert len(wall_cell_types) == len(wall_cell_rewards) \
== len(wall_cell_locs)
assert len(goal_cell_types) == len(goal_cell_rewards) \
== len(goal_cell_locs)
self.width = width
self.height = height
self.init_loc = init_loc
self.rand_init = rand_init
self.gamma = gamma
self.slip_prob = slip_prob
self.step_cost = step_cost
self.is_goal_terminal = is_goal_terminal
self.name = name
self.goal_cell_locs = goal_cell_locs
# Setup init location.
self.rand_init = rand_init
if rand_init:
init_loc = random.randint(1, width), random.randint(1, height)
while self.is_wall(*init_loc) or self.is_goal(*init_loc):
init_loc = random.randint(1, width), random.randint(1, height)
self.init_loc = init_loc
# Construct base class
MDP.__init__(self, NavigationWorldMDP.ACTIONS, self._transition_func,
self._reward_func,
init_state=NavigationWorldState(*init_loc),
gamma=gamma)
# Navigation MDP
self.__reset_nav_mdp()
self.__register_cell_types(nav_cell_types, wall_cell_types,
goal_cell_types)
self.__add_cells_by_locs(self.goal_cell_ids, goal_cell_locs,
NavigationWorldMDP.CELL_KIND_GOAL)
self.__add_cells_by_locs(self.wall_cell_ids, wall_cell_locs,
NavigationWorldMDP.CELL_KIND_WALL)
self.__add_nav_cells(self.nav_cell_ids, nav_cell_p_or_locs,
NavigationWorldMDP.CELL_KIND_NAV)
self.__check_state_map()
self.__register_cell_rewards(nav_cell_rewards,
wall_cell_rewards, goal_cell_rewards)
# Initialize value iteration object (computes reachable states)
self.value_iter = ValueIteration(self, sample_rate=1)
self._policy_invalidated = True
# ---------------------
# -- Navigation MDP --
# ---------------------
def _xy_to_rowcol(self, x, y):
"""Converts (x, y) to (row, col).
"""
return self.height - y, x - 1
def _rowcol_to_xy(self, row, col):
"""Converts (row, col) to (x, y).
"""
return col + 1, self.height - row
def __reset_cell_type_params(self):
self.__max_cell_id = -1
self.combined_cell_types = []
self.combined_cell_ids = []
self.cell_type_to_id = {}
self.cell_id_to_type = {}
def __reset_nav_mdp(self):
self.__reset_cell_type_params()
self.num_traj_init_states = None
self.feature_cell_dist = None
self.feature_cell_dist_kind = 0
self.nav_p_cell_ids = []
self.nav_p_cell_probs = []
self.__reset_state_map()
def __reset_state_map(self):
self.map_state_cell_id = -1 * np.ones((self.height, self.width),
dtype=np.int)
self.xy_to_cell_kind = defaultdict(lambda: "<Undefined>")
def __check_state_map(self):
if np.any(self.map_state_cell_id == -1):
raise ValueError("Some states have unassigned cell type!"
"Make sure each state of the MDP is covered by"
"a cell type. Check usage of probability values"
"in @nav_cell_p_or_locs.")
def __assign_cell_ids(self, cell_types):
n = len(cell_types)
cell_ids = list(
range(self.__max_cell_id + 1, self.__max_cell_id + 1 + n))
for cell_type, cell_id in zip(cell_types, cell_ids):
self.cell_type_to_id[cell_type] = cell_id
self.cell_id_to_type[cell_id] = cell_type
self.combined_cell_types += cell_types
self.combined_cell_ids += cell_ids
self.__max_cell_id += n
return cell_ids
def __register_cell_types(self, nav_cell_types, wall_cell_types,
goal_cell_types):
self.__reset_cell_type_params()
self.nav_cell_types = nav_cell_types
self.wall_cell_types = wall_cell_types
self.goal_cell_types = goal_cell_types
self.nav_cell_ids = self.__assign_cell_ids(nav_cell_types)
self.wall_cell_ids = self.__assign_cell_ids(wall_cell_types)
self.goal_cell_ids = self.__assign_cell_ids(goal_cell_types)
self.n_unique_cells = self.__max_cell_id + 1
def __add_cells_by_locs(self, cell_ids, cell_locs_list,
kind="<Undefined>"):
for idx, cell_id in enumerate(cell_ids):
cell_locs = cell_locs_list[idx]
assert isinstance(cell_locs, list)
for x, y in cell_locs:
r, c = self._xy_to_rowcol(x, y)
self.map_state_cell_id[r, c] = cell_id
self.xy_to_cell_kind[(x, y)] = kind
def __add_nav_cells(self, cell_ids, cell_p_or_locs_list,
kind="<Undefined>"):
self.nav_p_cell_ids = []
self.nav_p_cell_probs = []
for idx, cell_id in enumerate(cell_ids):
if isinstance(cell_p_or_locs_list[idx], list): # locations
cell_locs = cell_p_or_locs_list[idx]
for x, y in cell_locs:
r, c = self._xy_to_rowcol(x, y)
if self.map_state_cell_id[r, c] == -1:
self.map_state_cell_id[r, c] = cell_id
self.xy_to_cell_kind[(x, y)] = kind
else:
assert isinstance(cell_p_or_locs_list[idx],
float) # probability values
prob = cell_p_or_locs_list[idx]
self.nav_p_cell_ids.append(cell_id)
self.nav_p_cell_probs.append(prob)
assert round(sum(self.nav_p_cell_probs),
9) == 1, "Probability values must sum to 1."
for r in range(self.height):
for c in range(self.width):
if self.map_state_cell_id[r, c] == -1:
self.map_state_cell_id[r, c] = np.random.choice(
self.nav_p_cell_ids,
size=1,
p=self.nav_p_cell_probs)
x, y = self._rowcol_to_xy(r, c)
self.xy_to_cell_kind[(x, y)] = kind
def __register_cell_rewards(self, nav_cell_rewards,
wall_cell_rewards, goal_cell_rewards):
self.nav_cell_rewards = nav_cell_rewards
self.wall_cell_rewards = wall_cell_rewards
self.goal_cell_rewards = goal_cell_rewards
self.cell_type_rewards = nav_cell_rewards + \
wall_cell_rewards + \
goal_cell_rewards
def get_cell_id(self, x, y):
"""Get cell id of (x, y) location.
Returns:
A unique id assigned to each cell type.
"""
return self.map_state_cell_id[tuple(self._xy_to_rowcol(x, y))]
def is_wall(self, x, y):
"""Checks if (x,y) cell is wall or not.
Returns:
(bool): True iff (x, y) is a wall location.
"""
return self.get_state_kind(x, y) == NavigationWorldMDP.CELL_KIND_WALL
def is_goal(self, x, y):
"""Checks if (x,y) cell is goal or not.
Returns:
(bool): True iff (x, y) is a goal location.
"""
return self.get_state_kind(x, y) == NavigationWorldMDP.CELL_KIND_GOAL
def get_state_kind(self, x, y):
"""Returns kind of state at (x, y) location.
Returns:
(str): "wall", "nav", or "goal".
"""
return self.xy_to_cell_kind[(x, y)]
def _reset_goals(self, goal_cell_locs, goal_cell_rewards, goal_cell_types):
"""Resets the goals.
Resamples previous goal locations with navigation cells.
"""
# Re-sample old goal state cells
for r in range(self.height):
for c in range(self.width):
x, y = self._rowcol_to_xy(r, c)
if self.is_goal(x, y):
self.map_state_cell_id[r, c] = np.random.choice(
self.nav_p_cell_ids,
size=1,
p=self.nav_p_cell_probs)
self.xy_to_cell_kind[
(x, y)] = NavigationWorldMDP.CELL_KIND_NAV
self.__register_cell_types(self.nav_cell_types, self.wall_cell_types,
goal_cell_types)
self.__add_cells_by_locs(self.goal_cell_ids, goal_cell_locs,
NavigationWorldMDP.CELL_KIND_GOAL)
self.__check_state_map()
self.__register_cell_rewards(self.nav_cell_rewards,
self.wall_cell_rewards, goal_cell_rewards)
self._policy_invalidated = True
def _reset_rewards(self, nav_cell_rewards, wall_cell_rewards,
goal_cell_rewards):
"""Resets rewards corresponding to navigation, wall, and goal cells.
"""
self.__register_cell_rewards(nav_cell_rewards, wall_cell_rewards,
goal_cell_rewards)
self._policy_invalidated = True
# ---------
# -- MDP --
# ---------
def _is_goal_state_action(self, state, action):
"""
Args:
state (State)
action (str)
Returns:
(bool): True iff the state-action pair send the agent to
the goal state.
"""
if self.is_goal(state.x, state.y) == "goal" and self.is_goal_terminal:
# Already at terminal.
return False
if action == "left" and self.is_goal(state.x - 1, state.y):
return True
elif action == "right" and self.is_goal(state.x + 1, state.y):
return True
elif action == "down" and self.is_goal(state.x, state.y - 1):
return True
elif action == "up" and self.is_goal(state.x, state.y + 1):
return True
else:
return False
def _transition_func(self, state, action):
"""
Args:
state (State)
action (str)
Returns
(State)
"""
if state.is_terminal():
return state
r = random.random()
if self.slip_prob > r:
# Flip dir.
if action == "up":
action = random.choice(["left", "right"])
elif action == "down":
action = random.choice(["left", "right"])
elif action == "left":
action = random.choice(["up", "down"])
elif action == "right":
action = random.choice(["up", "down"])
if action == "up" and state.y < self.height and not self.is_wall(
state.x, state.y + 1):
next_state = NavigationWorldState(state.x, state.y + 1)
elif action == "down" and state.y > 1 and not self.is_wall(
state.x, state.y - 1):
next_state = NavigationWorldState(state.x, state.y - 1)
elif action == "right" and state.x < self.width and not self.is_wall(
state.x + 1, state.y):
next_state = NavigationWorldState(state.x + 1, state.y)
elif action == "left" and state.x > 1 and not self.is_wall(state.x - 1,
state.y):
next_state = NavigationWorldState(state.x - 1, state.y)
else:
next_state = NavigationWorldState(state.x, state.y)
if self.is_goal(next_state.x, next_state.y) and self.is_goal_terminal:
next_state.set_terminal(True)
return next_state
def _reward_func(self, state, action):
"""
Args:
state (State)
action (str)
Returns
(float)
"""
next_state = self._transition_func(state, action)
return self.cell_type_rewards[
self.get_cell_id(next_state.x, next_state.y)] - self.step_cost
# -------------------------
# -- Trajectory Sampling --
# -------------------------
def plan(self, state, policy=None, horizon=100):
'''
Args:
state (State)
policy (fn): S->A
horizon (int)
Returns:
(list): List of actions
'''
action_seq = []
state_seq = [state]
steps = 0
while (not state.is_terminal()) and steps < horizon:
next_action = policy(state)
action_seq.append(next_action)
state = self.transition_func(state, next_action)
state_seq.append(state)
steps += 1
return action_seq, state_seq
def set_traj_init_cell_types(self, cell_types):
"""
Sets cell types for sampling first state of trajectory
"""
self.traj_init_cell_row_idxs, self.traj_init_cell_col_idxs = [], []
for cell_type in cell_types:
rs, cs = np.where(self.map_state_cell_id ==
self.cell_type_to_id[cell_type])
self.traj_init_cell_row_idxs.extend(rs)
self.traj_init_cell_col_idxs.extend(cs)
self.num_traj_init_states = len(self.traj_init_cell_row_idxs)
def sample_traj_init_state(self, idx=None):
"""Samples trajectory init state (GridWorldState).
Type of init cells to be sampled can be specified using
set_traj_init_cell_types().
"""
if idx is None:
rand_idx = np.random.randint(len(self.traj_init_cell_row_idxs))
else:
assert 0 <= idx < len(self.traj_init_cell_row_idxs)
rand_idx = idx
x, y = self._rowcol_to_xy(self.traj_init_cell_row_idxs[rand_idx],
self.traj_init_cell_col_idxs[rand_idx])
return NavigationWorldState(x, y)
def sample_init_states(self, n, init_unique=False, skip_states=None):
"""Returns a list of init states (GridWorldState).
If init_unique is True, the max no. of states returned =
# of empty cells in the grid.
"""
assert n > 0
if init_unique:
c = 0
init_states_list = []
for idx in np.random.permutation(
len(self.traj_init_cell_row_idxs)):
state = self.sample_traj_init_state(idx)
if skip_states is None or state not in skip_states:
init_states_list.append(state)
c += 1
if c == n:
return init_states_list
return init_states_list
else:
return [self.sample_traj_init_state() for i in range(n)]
def sample_trajectories(self, n_traj, horizon, init_states=None,
init_cell_types=None, init_unique=False,
policy=None, rand_init_to_match_n_traj=True):
"""Samples trajectories.
Args:
n_traj: Number of trajectories to sample.
horizon (int): Planning horizon (max trajectory length).
init_states:
None - to use random init state
[GridWorldState(x,y),...] - to use specific init states
init_unique: When init_unique is set to False, this will sample
every possible init state and try to not repeat init state
unless @n_traj > @self.num_traj_init_states
policy (fn): S->A
rand_init_to_match_n_traj: If True, this will always return
@n_traj many trajectories. If # of unique states are less
than @n_traj, this will override the effect of @init_unique and
sample repeated init states.
Returns:
(traj_states_list, traj_actions_list) where
traj_states: [s1, s2, ..., sT]
traj_actions: [a1, a2, ..., aT]
"""
assert len(init_cell_types) >= 1
self.set_traj_init_cell_types(init_cell_types)
traj_states_list = []
traj_action_list = []
traj_init_states = []
if init_states is None:
# If no init_states are provided, sample n_traj many init states.
traj_init_states = self.sample_init_states(n_traj,
init_unique=init_unique)
else:
traj_init_states = copy.deepcopy(init_states)
if len(traj_init_states) >= n_traj:
traj_init_states = traj_init_states[:n_traj]
else:
# If # init_states < n_traj, sample remaining ones
if len(traj_init_states) < n_traj:
traj_init_states += self.sample_init_states(
n_traj - len(traj_init_states), init_unique=init_unique,
skip_states=traj_init_states)
# If rand_init_to_match_n_traj is set to True, sample more
# init_states if needed (may not be unique)
if rand_init_to_match_n_traj and len(traj_init_states) < n_traj:
traj_init_states += self.sample_init_states(n_traj,
init_unique=False)
if policy is None:
if len(self.goal_cell_locs) == 0:
print("Running value iteration with no goals assigned..")
policy = self.run_value_iteration().policy
for init_state in traj_init_states:
action_seq, state_seq = self.plan(init_state, policy=policy,
horizon=horizon)
traj_states_list.append(state_seq)
traj_action_list.append(action_seq)
return traj_states_list, traj_action_list
# ---------------------
# -- Value Iteration --
# ---------------------
def run_value_iteration(self):
"""Runs value iteration (if needed).
Returns:
ValueIteration object.
"""
# If value iteration was run previously, don't re-run it
if self._policy_invalidated == True:
self.value_iter = ValueIteration(self, sample_rate=1)
_ = self.value_iter.run_vi()
self._policy_invalidated = False
return self.value_iter
def get_value_grid(self):
"""Returns value over states space grid.
"""
value_iter = self.run_value_iteration()
V = np.zeros((self.height, self.width), dtype=np.float32)
for row in range(self.height):
for col in range(self.width):
x, y = self._rowcol_to_xy(row, col)
V[row, col] = value_iter.value_func[NavigationWorldState(x, y)]
return V
def get_all_states(self):
"""Returns all states.
"""
return [NavigationWorldState(x, y) for x in range(1, self.width + 1)
for y in range(1, self.height + 1)]
def get_reachable_states(self):
"""Returns all reachable states from @self.init_loc.
"""
return self.value_iter.get_states()
def get_trans_dict(self):
"""Returns transition dynamics matrix.
"""
self.value_iter._compute_matrix_from_trans_func()
return self.value_iter.trans_dict
# --------------
# -- Features --
# --------------
def __transfrom(self, mat, type):
if type == "normalize_manhattan":
return mat / (self.width + self.height)
if type == "normalize_euclidean":
return mat / np.sqrt(self.width**2 + self.height**2)
else:
return mat
def compute_grid_distance_features(self, incl_cells, incl_goals,
normalize=False):
"""Computes distances to specified cell types for entire grid.
Returns:
3D array (row, col, distance)
"""
# Convert 2 flags to decimal representation. This is useful to check if
# requested features are different from those previously stored
feature_kind = int(str(int(incl_cells)) + str(int(incl_goals)), 2)
if self.feature_cell_dist is not None \
and self.feature_cell_dist_kind == feature_kind:
return self.__transfrom(self.feature_cell_dist,
"normalize_manhattan" if normalize
else "None")
dist_cell_ids = copy.deepcopy(
self.nav_cell_ids) if incl_cells else []
dist_cell_ids += self.goal_cell_ids if incl_goals else []
loc_cells = [
np.vstack(np.where(self.map_state_cell_id == cell_id)).transpose()
for cell_id in dist_cell_ids]
self.feature_cell_dist = np.zeros(
self.map_state_cell_id.shape + (len(dist_cell_ids),), np.float32)
for row in range(self.height):
for col in range(self.width):
# Note: if particular cell type is missing in the grid, this
# will assign distance -1 to it
# Ord=1: Manhattan, Ord=2: Euclidean and so on
self.feature_cell_dist[row, col] = [
np.linalg.norm([row, col] - loc_cell, ord=1, axis=1).min()
if len(loc_cell) != 0 else -1 for loc_cell in loc_cells]
self.feature_cell_dist_kind = feature_kind
return self.__transfrom(self.feature_cell_dist,
"normalize_manhattan" if normalize else "None")
def cell_id_ind_feature(self, cell_id, include_goal=True):
"""Indicator feature for cell_id.
"""
if include_goal:
return np.eye(len(self.combined_cell_ids))[cell_id]
else:
# use 0 vector for goals
return np.vstack(
(np.eye(len(self.nav_cell_ids)),
np.zeros((len(self.wall_cell_ids), len(self.nav_cell_ids))),
np.zeros((len(self.goal_cell_ids), len(self.nav_cell_ids))))
)[cell_id]
def feature_at_loc(self, x, y, feature_type="indicator",
incl_cell_distances=False, incl_goal_indicator=True,
incl_goal_distances=False, normalize_distance=False,
dtype=np.float32):
"""Returns feature vector at a state corresponding to (x, y) location.
Args:
x, y (int, int): cartesian coordinates in 2d state space
feature_type (str): "indicator" to use one-hot encoding of
cell type "cartesian" to use (x, y) and "rowcol" to use
(row, col) as feature.
incl_cell_distances (bool): Include distance to each type of cell.
incl_goal_distances (bool): Include distance to the goals.
incl_goal_indicator: True - adds goal indicator feature
If all goals have same color, it'll add single indicator
variable for all goals, otherwise it'll use different
indicator variables for each goal.
(only applicable for "indicator" feature type).
normalize_distance (bool): Whether to normalize cell type
distances to 0-1 range (only used when
"incl_distance_features" is True).
dtype (numpy datatype): cast feature vector to dtype
"""
row, col = self._xy_to_rowcol(x, y)
assert feature_type in ["indicator", "cartesian", "rowcol"]
if feature_type == "indicator":
ind_feature = self.cell_id_ind_feature(
self.map_state_cell_id[row, col], incl_goal_indicator)
elif feature_type == "cartesian":
ind_feature = np.array([x, y])
elif feature_type == "rowcol":
ind_feature = np.array([row, col])
if incl_cell_distances or incl_goal_distances:
return np.hstack((ind_feature,
self.compute_grid_distance_features(
incl_cell_distances, incl_goal_distances,
normalize_distance)[row, col])).astype(dtype)
else:
return ind_feature.astype(dtype)
def feature_at_state(self, mdp_state, feature_type="indicator",
incl_cell_distances=False, incl_goal_indicator=True,
incl_goal_distances=False, normalize_distance=False,
dtype=np.float32):
"""Returns feature vector at a state corresponding to MdP State.
Args:
mdp_state (int, int): GridWorldState object
feature_type (str): "indicator" to use one-hot encoding of
cell type "cartesian" to use (x, y) and "rowcol" to use
(row, col) as feature.
incl_cell_distances (bool): Include distance to each type of cell.
incl_goal_distances (bool): Include distance to the goals.
incl_goal_indicator: True - adds goal indicator feature
If all goals have same color, it'll add single indicator
variable for all goals, otherwise it'll use different
indicator variables for each goal.
(only applicable for "indicator" feature type).
normalize_distance (bool): Whether to normalize cell type
distances to 0-1 range (only used when
"incl_distance_features" is True).
dtype (numpy datatype): cast feature vector to dtype
"""
return self.feature_at_loc(mdp_state.x, mdp_state.y, feature_type,
incl_cell_distances, incl_goal_indicator,
incl_goal_distances, normalize_distance,
dtype)
# -------------------
# -- Visualization --
# -------------------
@staticmethod
def __display_text(ax, x_start, x_end, y_start, y_end, values,
fontsize=12):
x_size = x_end - x_start
y_size = y_end - y_start
x_positions = np.linspace(start=x_start, stop=x_end, num=x_size,
endpoint=False)
y_positions = np.linspace(start=y_start, stop=y_end, num=y_size,
endpoint=False)
for y_index, y in enumerate(y_positions):
for x_index, x in enumerate(x_positions):
label = values[y_index, x_index]
ax.text(x, y, label, color='black', ha='center',
va='center', fontsize=fontsize)
def visualize_grid(self, values=None, cmap=cm.viridis, trajectories=None,
subplot_str=None, new_fig=True, show_colorbar=False,
show_rewards_colorbar=False, state_space_cmap=True,
init_marker=".k", traj_marker="-k",
text_values=None, text_size=10,
traj_linewidth=0.7, init_marker_sz=10,
goal_marker="*c", goal_marker_sz=10,
end_marker="", end_marker_sz=10,
axis_tick_font_sz=8, title=None):
"""Visualize Navigation World.
Args:
values (2d ndarray): Values to be visualized in the grid,
defaults to cell types.
cmap (Matplotlib Colormap): Colormap corresponding to values,
defaults to ListedColormap with colors specified in
@self.nav_cell_types and @self.goal_cell_types
trajectories: Trajectories to be shown on the grid.
subplot_str (str): Subplot number string (e.g., "411", "412", etc.)
new_fig (bool): Whether to use existing figure context.
show_rewards_colorbar (bool): Shows colorbar with cell
reward values.
title (str): Title of the plot.
"""
if new_fig:
plt.figure(
figsize=(max(self.height // 4, 6), max(self.width // 4, 6)))
# Subplot if needed
if subplot_str is not None:
plt.subplot(subplot_str)
# Use state space (cell types) if values is None
if values is None:
values = self.map_state_cell_id.copy()
# Colormap
if cmap is not None and state_space_cmap:
norm = colors.Normalize(vmin=0,
vmax=len(self.combined_cell_types)-1)
# Leave string colors as it is, convert int colors to
# normalized rgba
cell_colors = [
cmap(norm(cell)) if isinstance(cell, int) else cell
for cell in self.combined_cell_types]
cmap = colors.ListedColormap(cell_colors, N=self.n_unique_cells)
# Plot values
im = plt.imshow(values, interpolation='None', cmap=cmap)
plt.title(title if title else self.name)
ax = plt.gca()
ax.set_xticklabels('')
ax.set_yticklabels('')
ax.set_xticks(np.arange(self.width), minor=True)
ax.set_yticks(np.arange(self.height), minor=True)
ax.set_xticklabels(1 + np.arange(self.width), minor=True,
fontsize=axis_tick_font_sz)
ax.set_yticklabels(1 + np.arange(self.height)[::-1], minor=True,
fontsize=axis_tick_font_sz)
# Plot Trajectories
if trajectories is not None and len(trajectories) > 0:
for state_seq in trajectories:
if len(state_seq) == 0:
continue
path_xs = [s.x - 1 for s in state_seq]
path_ys = [self.height - (s.y) for s in state_seq]
plt.plot(path_xs, path_ys, traj_marker,
linewidth=traj_linewidth)
plt.plot(path_xs[0], path_ys[0], init_marker,
markersize=init_marker_sz) # Mark init state
plt.plot(path_xs[-1], path_ys[-1], end_marker,
markersize=end_marker_sz) # Mark end state
# Mark goals
if len(self.goal_cell_locs) != 0:
for goal_cells in self.goal_cell_locs:
for goal_x, goal_y in goal_cells:
plt.plot(goal_x - 1, self.height - goal_y, goal_marker,
markersize=goal_marker_sz)
# Text values on cell
if text_values is not None:
self.__display_text(ax, 0, self.width, 0, self.height, text_values,
fontsize=text_size)
# Colorbar
if show_colorbar:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.05)
if show_rewards_colorbar:
cb = plt.colorbar(im, ticks=range(len(self.cell_type_rewards)),
cax=cax)
cb.set_ticklabels(self.cell_type_rewards)
else:
plt.colorbar(im, cax=cax)
if subplot_str is None:
plt.show()
class NavigationMDP(GridWorldMDP):
'''
Class for Navigation MDP from:
MacGlashan, James, and Michael L. Littman. "Between Imitation and
Intention Learning." IJCAI. 2015.
'''
ACTIONS = ["up", "down", "left", "right"]
@staticmethod
def states_to_features(states, phi):
"""
Returns phi(states)
"""
return np.asarray([phi(s) for s in states], dtype=np.float32)
@staticmethod
def states_to_coord(states, phi):
"""
Returns phi(states)
"""
return np.asarray([(s.x, s.y) for s in states], dtype=np.float32)
def __init__(
self,
width=30,
height=30,
living_cell_types=["empty", "yellow", "red", "green", "purple"],
living_cell_rewards=[0, 0, -10, -10, -10],
living_cell_distribution="probability",
living_cell_type_probs=[0.68, 0.17, 0.05, 0.05, 0.05],
living_cell_locs=[
np.inf, np.inf, [(1, 1), (5, 5)], [(2, 2)], [4, 4]
],
goal_cell_locs=[],
goal_cell_rewards=[],
goal_cell_types=[],
gamma=0.99,
slip_prob=0.00,
step_cost=0.0,
is_goal_terminal=True,
traj_init_cell_types=[0],
planning_init_loc=(1, 1),
planning_rand_init=True,
name="Navigation MDP"):
"""
Note: Locations are specified in (x,y) format, but (row, col) convention
is used while storing in memory.
Args:
height (int): Height of navigation grid in no. of cells.
width (int): Width of navigation grid in no. of cells.
living_cell_types (list of cell types: [str, str, ...]): Non-goal cell types.
living_cell_rewards (list of int): Reward for each @cell_type.
living_cell_distribution (str):
"probability" - will assign cells according to @living_cell_type_probs.
"manual" - uses @living_cell_locs to assign cells to state space.
living_cell_type_probs (list of floats): Probability corresponding to
each @living_cell_types.
Note: Goal isn't factored so actual probabilities can off.
Default values are chosen arbitrarily larger than percolation threshold
for square lattice, which is just an approximation to match cell
distribution with that of the paper.
living_cell_locs (list of list of tuples
[[(x1,y1), (x2,y2)], [(x3,y3), ...], np.inf, ...}):
Specifies living cell locations. If elements are set to np.inf,
they will be sampled uniformly at random.
goal_cell_locs (list of tuples: [(int, int)...]): Goal locations.
goal_cell_rewards (list of int): Goal rewards.
goal_cell_types (list of str/int): Type of goal corresponding to @goal_cell_locs.
traj_init_cell_types (list of int): Trajectory init state sampling cell type
"""
assert height > 0 and isinstance(height, int) and width > 0 \
and isinstance(width,
int), "height and widht must be integers and > 0"
assert len(living_cell_types) == len(living_cell_rewards)
assert living_cell_distribution == "manual" or len(
living_cell_types) == len(living_cell_type_probs)
assert living_cell_distribution == "probability" or len(
living_cell_types) == len(living_cell_locs)
assert len(goal_cell_types) == len(goal_cell_locs) == len(
goal_cell_rewards)
GridWorldMDP.__init__(self,
width=width,
height=height,
init_loc=planning_init_loc,
rand_init=planning_rand_init,
goal_locs=goal_cell_locs,
lava_locs=[()],
walls=[],
is_goal_terminal=is_goal_terminal,
gamma=gamma,
init_state=None,
slip_prob=slip_prob,
step_cost=step_cost,
name=name)
# Living (navigation) cell types (str) and ids
self.living_cell_types = living_cell_types
self.living_cell_ids = list(range(len(living_cell_types)))
# State space (2d grid where each element holds a cell id)
self.state_space = self.__generate_state_space(
height, width, living_cell_distribution, living_cell_type_probs,
living_cell_locs)
# Preserve a copy without goals
self.state_space_wo_goals = self.state_space.copy()
# Rewards
self.living_cell_rewards = living_cell_rewards
self.state_rewards = np.asarray(
[[self.living_cell_rewards[item] for item in row]
for row in self.state_space]).reshape(height, width)
# Preserve a copy without goals
self.state_rewards_wo_goals = self.state_rewards.copy()
# Update cells and cell_rewards with goal and its rewards
self.reset_goals(goal_cell_locs, goal_cell_rewards, goal_cell_types)
# Find set of Empty/Navigable cells for sampling trajectory init state
self.set_traj_init_cell_types(cell_types=traj_init_cell_types)
# Run value iteration
self.value_iter = ValueIteration(self, sample_rate=1)
# Additional book-keeping
self.feature_cell_dist = None
self.feature_cell_dist_kind = 0
def get_states(self):
return self.value_iter.get_states()
def get_trans_dict(self):
self.value_iter._compute_matrix_from_trans_func()
return self.value_iter.trans_dict
def __generate_state_space(self, height, width, living_cell_distribution,
living_cell_type_probs, living_cell_locs):
assert living_cell_distribution in ["probability", "manual"]
# Assign cell type over state space
if living_cell_distribution == "probability":
cells = np.random.choice(len(living_cell_type_probs),
p=living_cell_type_probs,
replace=True,
size=(height, width))
else:
inf_cells = [
idx for idx, elem in enumerate(living_cell_locs)
if elem == np.inf
]
if len(inf_cells) == 0:
cells = -1 * np.ones((height, width), dtype=np.int)
else:
cells = np.random.choice(inf_cells,
p=[1. / len(inf_cells)] *
len(inf_cells),
replace=True,
size=(height, width))
for cell_type, cell_locs in enumerate(living_cell_locs):
if cell_type not in inf_cells:
for cell_loc in cell_locs:
row, col = self._xy_to_rowcol(cell_loc[0], cell_loc[1])
cells[row, col] = cell_type
# Additional check to ensure all states have corresponding cell type
assert np.any(cells == -1) == False, \
"Some grid cells have unassigned cell type! When you use manual " \
"distribution, make sure each state of the MPD is covered by a " \
"cell type. Check usage of np.inf in @living_cell_locs."
return cells
def _xy_to_rowcol(self, x, y):
"""
Converts (x,y) to (row,col)
"""
return self.height - y, x - 1
def _rowcol_to_xy(self, row, col):
"""
Converts (row,col) to (x,y)
"""
return col + 1, self.height - row
def _reward_func(self, state, action):
'''
Args:
state (State)
action (str)
Returns
(float)
'''
r, c = self._xy_to_rowcol(state.x, state.y)
if self._is_goal_state_action(state, action):
next_state = self._transition_func(state, action)
return self.goal_cell_rewards[self.goal_xy_to_idx[
(next_state.x, next_state.y)]] \
+ self.state_rewards[r, c] - self.step_cost
elif self.state_rewards[r, c] == 0:
return 0 - self.step_cost
else:
return self.state_rewards[r, c] - self.step_cost
def reset_goals(self, goal_cell_locs, goal_cell_rewards, goal_types):
"""
Resets the goals. Updates cell type grid and cell reward grid as per
new goal configuration.
"""
self.goal_cell_locs = goal_cell_locs
self.goal_cell_rewards = goal_cell_rewards
self.goal_cell_types = goal_types
self.goal_cell_ids = list(
range(self.living_cell_ids[-1] + 1,
self.living_cell_ids[-1] + 1 + len(self.goal_cell_locs)))
# Reset goal xy to idx dict
self.goal_xy_to_idx = {}
# Reset cell type and cell reward grid with no goals
self.state_space = self.state_space_wo_goals.copy()
self.state_rewards = self.state_rewards_wo_goals.copy()
# Update goals and their rewards
for idx, goal_loc in enumerate(self.goal_cell_locs):
goal_r, goal_c = self._xy_to_rowcol(goal_loc[0], goal_loc[1])
self.state_space[goal_r, goal_c] = self.goal_cell_ids[idx]
self.state_rewards[goal_r, goal_c] = self.goal_cell_rewards[idx]
self.goal_xy_to_idx[(goal_loc[0], goal_loc[1])] = idx
self.cell_ids = self.living_cell_ids + self.goal_cell_ids
self.cell_types = self.living_cell_types + self.goal_cell_types
self.cell_type_rewards = self.living_cell_rewards + self.goal_cell_rewards
if len(self.goal_cell_locs) != 0:
self._policy_invalidated = True
def set_traj_init_cell_types(self, cell_types=[0]):
"""
Sets cell types for sampling first state of trajectory
"""
self.traj_init_cell_row_idxs, self.traj_init_cell_col_idxs = [], []
for cell_type in cell_types:
rs, cs = np.where(self.state_space == cell_type)
self.traj_init_cell_row_idxs.extend(rs)
self.traj_init_cell_col_idxs.extend(cs)
self.num_traj_init_states = len(self.traj_init_cell_row_idxs)
def sample_empty_state(self, idx=None):
"""
Returns a random empty/white state of type GridWorldState()
"""
if idx is None:
rand_idx = np.random.randint(len(self.traj_init_cell_row_idxs))
else:
assert 0 <= idx < len(self.traj_init_cell_row_idxs)
rand_idx = idx
x, y = self._rowcol_to_xy(self.traj_init_cell_row_idxs[rand_idx],
self.traj_init_cell_col_idxs[rand_idx])
return GridWorldState(x, y)
def sample_init_states(self, n, repetition=False):
"""
Returns a list of random empty/white state of type GridWorldState()
Note: if repetition is False, the max no. of states returned = # of empty cells in the grid
"""
assert n > 0
if repetition is False:
return [
self.sample_empty_state(rand_idx)
for rand_idx in np.random.permutation(
len(self.traj_init_cell_row_idxs))[:n]
]
else:
return [self.sample_empty_state() for i in range(n)]
def plan(self, state, policy=None, horizon=100):
'''
Args:
state (State)
policy (fn): S->A
horizon (int)
Returns:
(list): List of actions
'''
action_seq = []
state_seq = [state]
steps = 0
while (not state.is_terminal()) and steps < horizon:
next_action = policy(state)
action_seq.append(next_action)
state = self.transition_func(state, next_action)
state_seq.append(state)
steps += 1
return action_seq, state_seq
def run_value_iteration(self):
"""
Runs value iteration (if needed) and returns ValueIteration object.
"""
# If value iteration was run previously, don't re-run it
if self._policy_invalidated == True:
_ = self.value_iter.run_vi()
self._policy_invalidated = False
return self.value_iter
def sample_data(self,
n_trajectory,
init_states=None,
init_repetition=False,
policy=None,
horizon=100,
pad_extra_trajectories=True,
map_actions_to_index=True):
"""
Args:
n_trajectory: number of trajectories to sample
init_states:
None - to use random init state
[GridWorldState(x,y),...] - to use specific init states
init_repetition: When init_state is set to None, this will sample
every possible init state and try to not repeat init state
unless @n_trajectory > @self.num_traj_init_states
policy (fn): S->A
horizon (int): planning horizon
pad_extra_trajectories: If True, this will always return
@n_trajectory many trajectories, overrides @init_repetition
if # unique states != @n_trajectory
value_iter_sampling_rate (int): Used for value iteration if policy
is set to None
map_actions_to_index (bool): Set True to get action indices in
trajectory
Returns:
(Traj_states, Traj_actions) where
Traj_states: [[s1, s2, ..., sT], [s4, s1, ..., sT], ...],
Traj_actions: [[a1, a2, ..., aT], [a4, a1, ..., aT], ...]
"""
a_s = []
d_mdp_states = []
visited_at_init = defaultdict(lambda: False)
action_to_idx = {a: i for i, a in enumerate(self.actions)}
if init_states is None:
init_states = self.sample_init_states(n_trajectory,
init_repetition)
if len(init_states) < n_trajectory and pad_extra_trajectories:
init_states += self.sample_init_states(n_trajectory -
len(init_states),
repetition=True)
else:
if len(init_states) < n_trajectory and pad_extra_trajectories:
# More init states need to be sampled
init_states += self.sample_init_states(
n_trajectory - len(init_states), init_repetition)
else:
# We have sufficient init states pre-specified, ignore the rest
# as we only need n_trajectory many
init_states = init_states[:n_trajectory]
if policy is None:
if len(self.goal_cell_locs) == 0:
raise ValueError("Cannot determine policy, no goals assigned!")
policy = self.run_value_iteration().policy
for init_state in init_states:
action_seq, state_seq = self.plan(init_state,
policy=policy,
horizon=horizon)
d_mdp_states.append(state_seq)
if map_actions_to_index:
a_s.append(action_seq)
else:
a_s.append([action_to_idx[a] for a in action_seq])
return d_mdp_states, a_s
def __transfrom(self, mat, type):
if type == "normalize_manhattan":
return mat / (self.width + self.height)
if type == "normalize_euclidean":
return mat / np.sqrt(self.width**2 + self.height**2)
else:
return mat
def compute_grid_distance_features(self,
incl_cells,
incl_goals,
normalize=False):
"""
Computes distances to specified cell types for entire grid.
Returns 3D array (row,col,distance)
"""
# Convert 2 flags to decimal representation. This is useful to check if
# requested features are different from those previously stored
feature_kind = int(str(int(incl_cells)) + str(int(incl_goals)), 2)
if self.feature_cell_dist is not None \
and self.feature_cell_dist_kind == feature_kind:
return self.__transfrom(
self.feature_cell_dist,
"normalize_manhattan" if normalize else "None")
dist_cell_types = copy.deepcopy(
self.living_cell_ids) if incl_cells else []
dist_cell_types += self.goal_cell_ids if incl_goals else []
loc_cells = [
np.vstack(np.where(self.state_space == cell)).transpose()
for cell in dist_cell_types
]
self.feature_cell_dist = np.zeros(
self.state_space.shape + (len(dist_cell_types), ), np.float32)
for row in range(self.height):
for col in range(self.width):
# Note: if particular cell type is missing in the grid, this
# will assign distance -1 to it
# Ord=1: Manhattan, Ord=2: Euclidean and so on
self.feature_cell_dist[row, col] = [
np.linalg.norm([row, col] - loc_cell, ord=1, axis=1).min()
if len(loc_cell) != 0 else -1 for loc_cell in loc_cells
]
self.feature_cell_dist_kind = feature_kind
return self.__transfrom(self.feature_cell_dist,
"normalize_manhattan" if normalize else "None")
def feature_at_loc(self,
x,
y,
feature_type="indicator",
incl_cell_distances=False,
incl_goal_indicator=False,
incl_goal_distances=False,
normalize_distance=False,
dtype=np.float32):
"""
Returns feature vector at a state corresponding to (x,y) location
Args:
x, y (int, int): cartesian coordinates in 2d state space
feature_type (str): "indicator" to use one-hot encoding of cell type
"cartesian" to use (x,y) and "rowcol" to use (row, col) as feature
incl_cell_distances (bool): Include distance to each type of cell.
incl_goal_distances (bool): Include distance to the goals.
incl_goal_indicator: True - adds goal indicator feature
If all goals have same color, it'll add single indicator variable
for all goals, otherwise it'll use different indicator variables
for each goal.
(only applicable for "indicator" feature type).
normalize_distance (bool): Whether to normalize cell type distances
to 0-1 range (only used when "incl_distance_features" is True).
dtype (numpy datatype): cast feature vector to dtype
"""
row, col = self._xy_to_rowcol(x, y)
assert feature_type in ["indicator", "cartesian", "rowcol"]
if feature_type == "indicator":
if incl_goal_indicator:
ind_feature = np.eye(len(self.cell_ids))[self.state_space[row,
col]]
else:
if (x, y) in self.goal_cell_locs:
ind_feature = np.zeros(len(self.living_cell_ids))
else:
ind_feature = np.eye(len(
self.living_cell_ids))[self.state_space[row, col]]
elif feature_type == "cartesian":
ind_feature = np.array([x, y])
elif feature_type == "rowcol":
ind_feature = np.array([row, col])
if incl_cell_distances or incl_goal_distances:
return np.hstack((ind_feature,
self.compute_grid_distance_features(
incl_cell_distances, incl_goal_distances,
normalize_distance)[row, col])).astype(dtype)
else:
return ind_feature.astype(dtype)
def feature_at_state(self,
mdp_state,
feature_type="indicator",
incl_cell_distances=False,
incl_goal_indicator=False,
incl_goal_distances=False,
normalize_distance=False,
dtype=np.float32):
"""
Returns feature vector at a state corresponding to MdP State
Args:
mdp_state (int, int): GridWorldState object
feature_type (str): "indicator" to use one-hot encoding of cell type
"cartesian" to use (x,y) and "rowcol" to use (row, col) as feature
incl_cell_distances (bool): Include distance to each type of cell.
incl_goal_distances (bool): Include distance to the goals.
incl_goal_indicator: True - adds goal indicator feature
If all goals have same color, it'll add single indicator variable
for all goals, otherwise it'll use different indicator variables
for each goal.
(only applicable for "indicator" feature type).
normalize_distance (bool): Whether to normalize cell type distances
to 0-1 range (only used when "incl_distance_features" is True).
dtype (numpy datatype): cast feature vector to dtype
"""
return self.feature_at_loc(mdp_state.x, mdp_state.y, feature_type,
incl_cell_distances, incl_goal_indicator,
incl_goal_distances, normalize_distance,
dtype)
def __display_text(self,
ax,
x_start,
x_end,
y_start,
y_end,
values,
fontsize=12):
"""
Ref: https://stackoverflow.com/questions/33828780/matplotlib-display-array-values-with-imshow
"""
x_size = x_end - x_start
y_size = y_end - y_start
x_positions = np.linspace(start=x_start,
stop=x_end,
num=x_size,
endpoint=False)
y_positions = np.linspace(start=y_start,
stop=y_end,
num=y_size,
endpoint=False)
for y_index, y in enumerate(y_positions):
for x_index, x in enumerate(x_positions):
label = values[y_index, x_index]
ax.text(x,
y,
label,
color='black',
ha='center',
va='center',
fontsize=fontsize)
def visualize_grid(self,
values=None,
cmap=None,
trajectories=None,
subplot_str=None,
new_fig=True,
show_colorbar=False,
show_rewards_colorbar=False,
int_cells_cmap=cm.viridis,
init_marker=".k",
traj_marker="-k",
text_values=None,
text_size=10,
traj_linewidth=0.7,
init_marker_sz=10,
goal_marker="*c",
goal_marker_sz=10,
end_marker="",
end_marker_sz=10,
axis_tick_font_sz=8,
title="Navigation MDP"):
"""
Args:
values (2d ndarray): Values to be visualized in the grid,
defaults to cell types.
cmap (Matplotlib Colormap): Colormap corresponding to values,
defaults to ListedColormap with colors specified in
@self.living_cell_types and @self.goal_cell_types
trajectories: Trajectories to be shown on the grid.
subplot_str (str): Subplot number string (e.g., "411", "412", etc.)
new_fig (bool): Whether to use existing figure context.
show_rewards_colorbar (bool): Shows colorbar with cell reward values.
title (str): Title of the plot.
"""
if new_fig == True:
plt.figure(figsize=(max(self.height // 4, 6),
max(self.width // 4, 6)))
# Subplot if needed
if subplot_str is not None:
plt.subplot(subplot_str)
# Colormap
if cmap is None:
norm = colors.Normalize(vmin=0, vmax=len(self.cell_types) - 1)
# Leave string colors as it is, convert int colors to normalized rgba
cell_colors = [
int_cells_cmap(norm(cell)) if isinstance(cell, int) else cell
for cell in self.cell_types
]
cmap = colors.ListedColormap(cell_colors)
if values is None:
values = self.state_space.copy()
# Plot values
im = plt.imshow(values, interpolation='None', cmap=cmap)
plt.title(title)
ax = plt.gca()
ax.set_xticklabels('')
ax.set_yticklabels('')
ax.set_xticks(np.arange(self.width), minor=True)
ax.set_yticks(np.arange(self.height), minor=True)
ax.set_xticklabels(1 + np.arange(self.width),
minor=True,
fontsize=axis_tick_font_sz)
ax.set_yticklabels(1 + np.arange(self.height)[::-1],
minor=True,
fontsize=axis_tick_font_sz)
# Plot Trajectories
if trajectories is not None and len(trajectories) > 0:
for state_seq in trajectories:
if len(state_seq) == 0:
continue
path_xs = [s.x - 1 for s in state_seq]
path_ys = [self.height - (s.y) for s in state_seq]
plt.plot(path_xs,
path_ys,
traj_marker,
linewidth=traj_linewidth)
plt.plot(path_xs[0],
path_ys[0],
init_marker,
markersize=init_marker_sz) # Mark init state
plt.plot(path_xs[-1],
path_ys[-1],
end_marker,
markersize=end_marker_sz) # Mark end state
# Mark goals
if len(self.goal_cell_locs) != 0:
for goal_x, goal_y in self.goal_cell_locs:
plt.plot(goal_x - 1,
self.height - goal_y,
goal_marker,
markersize=goal_marker_sz)
# Text values on cell
if text_values is not None:
self.__display_text(ax,
0,
self.width,
0,
self.height,
text_values,
fontsize=text_size)
# Colorbar
if show_colorbar:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.05)
if show_rewards_colorbar:
cb = plt.colorbar(im,
ticks=range(len(self.cell_type_rewards)),
cax=cax)
cb.set_ticklabels(self.cell_type_rewards)
else:
plt.colorbar(im, cax=cax)
if subplot_str is None:
plt.show()
