def main(): # Setup MDP, Agents. size = 5 agent = { "x": 1, "y": 1, "dx": 1, "dy": 0, "dest_x": size, "dest_y": size, "has_block": 0 } blocks = [{"x": size, "y": 1}] lavas = [{ "x": x, "y": y } for x, y in map(lambda z: (z + 1, (size + 1) / 2), xrange(size))] mdp = TrenchOOMDP(size, size, agent, blocks, lavas) ql_agent = QLearnerAgent(actions=mdp.get_actions()) rand_agent = RandomAgent(actions=mdp.get_actions()) # Run experiment and make plot. # run_agents_on_mdp([ql_agent, rand_agent], mdp, instances=30, episodes=250, steps=250) vi = ValueIteration(mdp, delta=0.0001, max_iterations=5000) iters, val = vi.run_vi() print " done." states = vi.get_states() num_states = len(states) print num_states, states
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 MonotoneUpperBound(Planner): def __init__(self, mdp, name='MonotoneUpperBound'): Planner.__init__(self, mdp, name) self.vi = ValueIteration(mdp) self.states = self.vi.get_states() self.upper_values = self._construct_upper_values() def _construct_upper_values(self): values = defaultdict() for state in self.states: values[state] = 1. / (1. - self.gamma) return values
class MonotoneUpperBound(Planner): def __init__(self, mdp, name='MonotoneUpperBound'): Planner.__init__(self, mdp, name) self.vi = ValueIteration(mdp) self.states = self.vi.get_states() self.upper_values = self._construct_upper_values() def _construct_upper_values(self): values = defaultdict() for state in self.states: values[state] = 1. / (1. - self.gamma) return values
def make_multitask_sa_info_sa(mdp_distr, beta, is_deterministic_ib=False): ''' Args: mdp_distr (simple_rl.MDPDistribution) beta (float) is_deterministic_ib (float) Returns: (simple_rl.StateAbstraction) ''' master_sa = None all_state_absr = [] for mdp in mdp_distr.get_all_mdps(): # Get demo policy. vi = ValueIteration(mdp) vi.run_vi() demo_policy = get_lambda_policy( make_det_policy_eps_greedy(vi.policy, vi.get_states(), mdp.get_actions(), epsilon=0.2)) # Get abstraction. pmf_s_phi, phi_pmf, abstr_policy_pmf = run_info_sa( mdp, demo_policy, beta=beta, is_deterministic_ib=is_deterministic_ib) crisp_sa = convert_prob_sa_to_sa(ProbStateAbstraction(phi_pmf)) all_state_absr.append(crisp_sa) # Make master state abstr by intersection. vi = ValueIteration(mdp_distr.get_all_mdps()[0]) ground_states = vi.get_states() master_sa = sa_helpers.merge_state_abstr(all_state_absr, ground_states) return master_sa
def _make_mini_mdp_option_policy(mini_mdp): ''' Args: mini_mdp (MDP) Returns: Policy ''' # Solve the MDP defined by the terminal abstract state. mini_mdp_vi = ValueIteration(mini_mdp, delta=0.005, max_iterations=500, sample_rate=20) iters, val = mini_mdp_vi.run_vi() o_policy_dict = make_dict_from_lambda(mini_mdp_vi.policy, mini_mdp_vi.get_states()) o_policy = PolicyFromDict(o_policy_dict) return o_policy.get_action, mini_mdp_vi
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 main(): # Make MDP. grid_dim = 11 mdp = FourRoomMDP(width=grid_dim, height=grid_dim, init_loc=(1, 1), slip_prob=0.05, goal_locs=[(grid_dim, grid_dim)], gamma=0.99) # Experiment Type. exp_type = "learn_w_abstr" # For comparing policies and visualizing. beta = 1 is_deterministic_ib = True is_agent_in_control = True # For main plotting experiment. beta_range = list(chart_utils.drange(0.0, 4.0, 1.0)) instances = 1 # Get demo policy. vi = ValueIteration(mdp) _, val = vi.run_vi() # Epsilon greedy policy demo_policy = get_lambda_policy(make_det_policy_eps_greedy(vi.policy, vi.get_states(), mdp.get_actions(), epsilon=0.1)) if exp_type == "plot_info_sa_val_and_num_states": # Makes the main two plots. make_info_sa_val_and_size_plots(mdp, demo_policy, beta_range, instances=instances, is_agent_in_control=is_agent_in_control) elif exp_type == "compare_policies": # Makes a plot comparing value of pi-phi combo from info_sa with \pi_d. info_sa_compare_policies(mdp, demo_policy, beta=beta, is_deterministic_ib=is_deterministic_ib, is_agent_in_control=is_agent_in_control) elif exp_type == "visualize_info_sa_abstr": # Visualize the state abstraction found by info_sa. info_sa_visualize_abstr(mdp, demo_policy, beta=beta, is_deterministic_ib=is_deterministic_ib, is_agent_in_control=is_agent_in_control) elif exp_type == "learn_w_abstr": # Run learning experiments for different settings of \beta. learn_w_abstr(mdp, demo_policy, is_deterministic_ib=is_deterministic_ib) elif exp_type == "planning": info_sa_planning_experiment()
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
def run_info_sa(mdp, demo_policy_lambda, iters=500, beta=20.0, convergence_threshold=0.01, is_deterministic_ib=False): ''' Args: mdp (simple_rl.MDP) demo_policy (lambda : simple_rl.State --> str) iters (int) beta (float) convergence_threshold (float): When all three distributions satisfy L1(p_{t+1}, p_t) < @convergence_threshold, we stop iterating. is_deterministic_ib (bool): If true, run DIB, else IB. Returns: (dict): P(s_phi) (dict): P(s_phi | s) (dict): P(a | s_phi) Summary: Runs the Blahut-Arimoto like algorithm for the given mdp. ''' print "~"*16 print "~~ BETA =", beta, "~~" print "~"*16 # Get state and action space. vi = ValueIteration(mdp) ground_states = vi.get_states() num_ground_states = len(ground_states) actions = mdp.get_actions() # Get pmf demo policy and stationary distribution. demo_policy_pmf = get_pmf_policy(demo_policy_lambda, ground_states, actions) pmf_s = get_stationary_rho_from_policy(demo_policy_lambda, mdp, ground_states) # Init distributions. phi_pmf, abstract_states = init_random_phi(ground_states, deterministic=is_deterministic_ib) pmf_s_phi = compute_prob_of_s_phi(pmf_s, phi_pmf, ground_states, abstract_states) abstr_policy_pmf = compute_abstr_policy(demo_policy_pmf, ground_states, abstract_states, actions, phi_pmf, pmf_s, pmf_s_phi, deterministic=is_deterministic_ib) # info_sa. for i in range(iters): # print 'Iteration {0} of {1}'.format(i+1, iters) # (A) Compute \phi. next_phi_pmf = compute_phi_pmf(pmf_s, pmf_s_phi, demo_policy_pmf, abstr_policy_pmf, ground_states, abstract_states, beta=beta, deterministic=is_deterministic_ib) # (B) Compute \rho(s). next_pmf_s_phi = compute_prob_of_s_phi(pmf_s, next_phi_pmf, ground_states, abstract_states) # (C) Compute \pi_\phi. next_abstr_policy_pmf = compute_abstr_policy(demo_policy_pmf, ground_states, abstract_states, actions, next_phi_pmf, pmf_s, next_pmf_s_phi, deterministic=is_deterministic_ib) # Convergence checks. coding_update_delta = max([l1_distance(next_phi_pmf[s], phi_pmf[s]) for s in ground_states]) policy_update_delta = max([l1_distance(next_abstr_policy_pmf[s_phi], abstr_policy_pmf[s_phi]) for s_phi in abstract_states]) state_distr_update_delta = l1_distance(next_pmf_s_phi, pmf_s_phi) # Debugging. is_coding_converged = coding_update_delta < convergence_threshold is_policy_converged = policy_update_delta < convergence_threshold is_pmf_s_phi_converged = state_distr_update_delta < convergence_threshold # Update pointers. phi_pmf = next_phi_pmf pmf_s_phi = next_pmf_s_phi abstr_policy_pmf = next_abstr_policy_pmf if is_coding_converged and is_policy_converged and is_pmf_s_phi_converged: print "\tinfo_sa Converged." break return pmf_s_phi, phi_pmf, abstr_policy_pmf
def _draw_state(screen, taxi_oomdp, state, policy=None, action_char_dict={}, show_value=False, agent=None, draw_statics=True, agent_shape=None): ''' Args: screen (pygame.Surface) taxi_oomdp (TaxiOOMDP) state (State) agent_shape (pygame.rect) Returns: (pygame.Shape) ''' # Make value dict. val_text_dict = defaultdict(lambda: defaultdict(float)) if show_value: if agent is not None: if agent.name == 'Q-learning': # Use agent value estimates. for s in agent.q_func.keys(): val_text_dict[s.get_agent_x()][ s.get_agent_y()] = agent.get_value(s) # slightly abusing the distinction between agents and planning modules... else: for s in taxi_oomdp.get_states(): val_text_dict[s.get_agent_x()][ s.get_agent_y()] = agent.get_value(s) else: # Use Value Iteration to compute value. vi = ValueIteration(taxi_oomdp, sample_rate=10) vi.run_vi() for s in vi.get_states(): val_text_dict[s.get_agent_x()][s.get_agent_y()] = vi.get_value( s) # Make policy dict. policy_dict = defaultdict(lambda: defaultdict(str)) if policy: for s in taxi_oomdp.get_states(): policy_dict[s.get_agent_x()][s.get_agent_y()] = policy(s) # Prep some dimensions to make drawing easier. scr_width, scr_height = screen.get_width(), screen.get_height() width_buffer = scr_width / 10.0 height_buffer = 30 + (scr_height / 10.0) # Add 30 for title. cell_width = (scr_width - width_buffer * 2) / taxi_oomdp.width cell_height = (scr_height - height_buffer * 2) / taxi_oomdp.height objects = state.get_objects() agent_x, agent_y = objects["agent"][0]["x"], objects["agent"][0]["y"] font_size = int(min(cell_width, cell_height) / 4.0) reg_font = pygame.font.SysFont("CMU Serif", font_size) cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2) if agent_shape is not None: # Clear the old shape. pygame.draw.rect(screen, (255, 255, 255), agent_shape) # Statics if draw_statics: # Draw walls. for w in taxi_oomdp.walls: w_x, w_y = w["x"], w["y"] top_left_point = width_buffer + cell_width * ( w_x - 1) + 5, height_buffer + cell_height * ( taxi_oomdp.height - w_y) + 5 pygame.draw.rect( screen, (46, 49, 49), top_left_point + (cell_width - 10, cell_height - 10), 0) # Draw the destination. for i, p in enumerate(objects["passenger"]): # Dest. dest_x, dest_y = p["dest_x"], p["dest_y"] top_left_point = int(width_buffer + cell_width * (dest_x - 1) + 38), int(height_buffer + cell_height * (taxi_oomdp.height - dest_y) + 18) passenger_size = cell_width / 11 # purple dest_col = (188, 30, 230) n, r = 6, passenger_size x, y = top_left_point[0], top_left_point[1] color = dest_col pygame.draw.polygon(screen, color, [(x + r * math.cos(2 * math.pi * i / n), y + r * math.sin(2 * math.pi * i / n)) for i in range(n)]) # Draw new agent. top_left_point = width_buffer + cell_width * ( agent_x - 1), height_buffer + cell_height * (taxi_oomdp.height - agent_y) agent_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) agent_shape = _draw_agent(agent_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 4) for i, p in enumerate(objects["passenger"]): # Dest. x, y = p["x"], p["y"] passenger_size = cell_width / 11 if p["in_taxi"]: top_left_point = int(width_buffer + cell_width * (x - 1) + passenger_size + 58), int(height_buffer + cell_height * (taxi_oomdp.height - y) + passenger_size + 15) else: top_left_point = int(width_buffer + cell_width * (x - 1) + passenger_size + 25), int(height_buffer + cell_height * (taxi_oomdp.height - y) + passenger_size + 34) # light green dest_col = (59, 189, 23) n, r = 6, passenger_size x, y = top_left_point[0], top_left_point[1] color = dest_col pygame.draw.polygon(screen, color, [(x + r * math.cos(2 * math.pi * i / n), y + r * math.sin(2 * math.pi * i / n)) for i in range(n)]) if draw_statics: # For each row: for i in range(taxi_oomdp.width): # For each column: for j in range(taxi_oomdp.height): top_left_point = width_buffer + cell_width * i, height_buffer + cell_height * j r = pygame.draw.rect( screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3) # Show value of states. if show_value and not taxi_helpers.is_wall( taxi_oomdp, i + 1, taxi_oomdp.height - j): # Draw the value. val = val_text_dict[i + 1][taxi_oomdp.height - j] color = mdpv.val_to_color(val) pygame.draw.rect( screen, color, top_left_point + (cell_width, cell_height), 0) value_text = reg_font.render(str(round(val, 2)), True, (46, 49, 49)) text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int(top_left_point[1] + cell_height / 3.0) screen.blit(value_text, text_center_point) # Show optimal action to take in each grid cell. if policy and not taxi_helpers.is_wall(taxi_oomdp, i + 1, taxi_oomdp.height - j): a = policy_dict[i + 1][taxi_oomdp.height - j] if a not in action_char_dict: text_a = a else: text_a = action_char_dict[a] text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int(top_left_point[1] + cell_height / 3.0) text_rendered_a = cc_font.render(text_a, True, (46, 49, 49)) screen.blit(text_rendered_a, text_center_point) pygame.display.flip() return agent_shape
def _draw_state(screen, grid_mdp, state, policy=None, action_char_dict={}, show_value=False, agent=None, draw_statics=False, agent_shape=None): ''' Args: screen (pygame.Surface) grid_mdp (MDP) state (State) show_value (bool) agent (Agent): Used to show value, by default uses VI. draw_statics (bool) agent_shape (pygame.rect) Returns: (pygame.Shape) ''' # Make value dict. val_text_dict = defaultdict(lambda: defaultdict(float)) if show_value: if agent is not None: # Use agent value estimates. for s in agent.q_func.keys(): val_text_dict[s.x][s.y] = agent.get_value(s) else: # Use Value Iteration to compute value. vi = ValueIteration(grid_mdp) vi.run_vi() for s in vi.get_states(): val_text_dict[s.x][s.y] = vi.get_value(s) # Make policy dict. policy_dict = defaultdict(lambda: defaultdict(str)) if policy: vi = ValueIteration(grid_mdp) vi.run_vi() for s in vi.get_states(): policy_dict[s.x][s.y] = policy(s) # Prep some dimensions to make drawing easier. scr_width, scr_height = screen.get_width(), screen.get_height() width_buffer = scr_width / 10.0 height_buffer = 30 + (scr_height / 10.0) # Add 30 for title. cell_width = (scr_width - width_buffer * 2) / grid_mdp.width cell_height = (scr_height - height_buffer * 2) / grid_mdp.height goal_locs = grid_mdp.get_goal_locs() lava_locs = grid_mdp.get_lava_locs() font_size = int(min(cell_width, cell_height) / 4.0) reg_font = pygame.font.SysFont("CMU Serif", font_size) cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2) # Draw the static entities. if draw_statics: # For each row: for i in range(grid_mdp.width): # For each column: for j in range(grid_mdp.height): top_left_point = width_buffer + cell_width * i, height_buffer + cell_height * j r = pygame.draw.rect( screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3) if policy and not grid_mdp.is_wall(i + 1, grid_mdp.height - j): a = policy_dict[i + 1][grid_mdp.height - j] if a not in action_char_dict: text_a = a else: text_a = action_char_dict[a] text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int(top_left_point[1] + cell_height / 3.0) text_rendered_a = cc_font.render(text_a, True, (46, 49, 49)) screen.blit(text_rendered_a, text_center_point) if show_value and not grid_mdp.is_wall(i + 1, grid_mdp.height - j): # Draw the value. val = val_text_dict[i + 1][grid_mdp.height - j] color = mdpv.val_to_color(val) pygame.draw.rect( screen, color, top_left_point + (cell_width, cell_height), 0) # text_center_point = int(top_left_point[0] + cell_width/2.0 - 10), int(top_left_point[1] + cell_height/7.0) # text = str(round(val,2)) # text_rendered = reg_font.render(text, True, (46, 49, 49)) # screen.blit(text_rendered, text_center_point) if grid_mdp.is_wall(i + 1, grid_mdp.height - j): # Draw the walls. top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5 r = pygame.draw.rect( screen, (94, 99, 99), top_left_point + (cell_width - 10, cell_height - 10), 0) if (i + 1, grid_mdp.height - j) in goal_locs: # Draw goal. circle_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) circler_color = (154, 195, 157) pygame.draw.circle(screen, circler_color, circle_center, int(min(cell_width, cell_height) / 3.0)) if (i + 1, grid_mdp.height - j) in lava_locs: # Draw goal. circle_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) circler_color = (224, 145, 157) pygame.draw.circle(screen, circler_color, circle_center, int(min(cell_width, cell_height) / 4.0)) # Current state. if not show_value and (i + 1, grid_mdp.height - j) == ( state.x, state.y) and agent_shape is None: tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) agent_shape = _draw_agent( tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 8) if agent_shape is not None: # Clear the old shape. pygame.draw.rect(screen, (255, 255, 255), agent_shape) top_left_point = width_buffer + cell_width * ( state.x - 1), height_buffer + cell_height * (grid_mdp.height - state.y) tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) # Draw new. # if not show_value or policy is not None: agent_shape = _draw_agent( tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 16) pygame.display.flip() return agent_shape
def _draw_state(screen, two_goal_oomdp, state, policy=None, action_char_dict={}, show_value=False, agent=None, draw_statics=True, agent_shape=None): ''' Args: screen (pygame.Surface) two_goal_oomdp (TwoGoalOOMDP) state (State) agent_shape (pygame.rect) Returns: (pygame.Shape) ''' # Make value dict. val_text_dict = defaultdict(lambda: defaultdict(float)) if show_value: if agent is not None: if agent.name == 'Q-learning': # Use agent value estimates. for s in agent.q_func.keys(): val_text_dict[s.get_agent_x()][s.get_agent_y()] = agent.get_value(s) # slightly abusing the distinction between agents and planning modules... else: for s in two_goal_oomdp.get_states(): val_text_dict[s.get_agent_x()][s.get_agent_y()] = agent.get_value(s) else: # Use Value Iteration to compute value. vi = ValueIteration(two_goal_oomdp, sample_rate=10) vi.run_vi() for s in vi.get_states(): val_text_dict[s.get_agent_x()][s.get_agent_y()] = vi.get_value(s) # Make policy dict. policy_dict = defaultdict(lambda : defaultdict(str)) if policy: for s in two_goal_oomdp.get_states(): policy_dict[s.get_agent_x()][s.get_agent_y()] = policy(s) # Prep some dimensions to make drawing easier. scr_width, scr_height = screen.get_width(), screen.get_height() width_buffer = scr_width / 10.0 height_buffer = 30 + (scr_height / 10.0) # Add 30 for title. cell_width = (scr_width - width_buffer * 2) / two_goal_oomdp.width cell_height = (scr_height - height_buffer * 2) / two_goal_oomdp.height objects = state.get_objects() agent_x, agent_y = objects["agent"][0]["x"], objects["agent"][0]["y"] font_size = int(min(cell_width, cell_height) / 4.0) reg_font = pygame.font.SysFont("CMU Serif", font_size) cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2) if agent_shape is not None: # Clear the old shape. pygame.draw.rect(screen, (255,255,255), agent_shape) # Statics if draw_statics: # Draw walls. for w in two_goal_oomdp.walls: w_x, w_y = w["x"], w["y"] top_left_point = width_buffer + cell_width * (w_x - 1) + 5, height_buffer + cell_height * ( two_goal_oomdp.height - w_y) + 5 pygame.draw.rect(screen, (46, 49, 49), top_left_point + (cell_width - 10, cell_height - 10), 0) # Draw the two goals col_idxs = [-2, 0] for i, g in enumerate(two_goal_oomdp.goals): dest_x, dest_y = g["x"], g["y"] top_left_point = int(width_buffer + cell_width*(dest_x - 1) + 75), int(height_buffer + cell_height*(two_goal_oomdp.height - dest_y) + 65) dest_col = (int(max(color_ls[col_idxs[i]][0]-30, 0)), int(max(color_ls[col_idxs[i]][1]-30, 0)), int(max(color_ls[col_idxs[i]][2]-30, 0))) center = top_left_point + (cell_width / 2, cell_height / 2) radius = 45 iterations = 150 for i in range(iterations): ang = i * 3.14159 * 2 / iterations dx = int(math.cos(ang) * radius) dy = int(math.sin(ang) * radius) x = center[0] + dx y = center[1] + dy pygame.draw.circle(screen, dest_col, (x, y), 5) # Draw new agent. top_left_point = width_buffer + cell_width * (agent_x - 1), height_buffer + cell_height * ( two_goal_oomdp.height - agent_y) agent_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) agent_shape = _draw_agent(agent_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 4) if draw_statics: # For each row: for i in range(two_goal_oomdp.width): # For each column: for j in range(two_goal_oomdp.height): top_left_point = width_buffer + cell_width*i, height_buffer + cell_height*j r = pygame.draw.rect(screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3) # Show value of states. if show_value and not two_goal_helpers.is_wall(two_goal_oomdp, i + 1, two_goal_oomdp.height - j): # Draw the value. val = val_text_dict[i + 1][two_goal_oomdp.height - j] color = mdpv.val_to_color(val) pygame.draw.rect(screen, color, top_left_point + (cell_width, cell_height), 0) value_text = reg_font.render(str(round(val, 2)), True, (46, 49, 49)) text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int( top_left_point[1] + cell_height / 3.0) screen.blit(value_text, text_center_point) # Show optimal action to take in each grid cell. if policy and not two_goal_helpers.is_wall(two_goal_oomdp, i + 1, two_goal_oomdp.height - j): a = policy_dict[i+1][two_goal_oomdp.height - j] if a not in action_char_dict: text_a = a else: text_a = action_char_dict[a] text_center_point = int(top_left_point[0] + cell_width/2.0 - 10), int(top_left_point[1] + cell_height/3.0) text_rendered_a = cc_font.render(text_a, True, (46, 49, 49)) screen.blit(text_rendered_a, text_center_point) pygame.display.flip() return agent_shape
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()
def draw_state(screen, cleanup_mdp, state, policy=None, action_char_dict={}, show_value=False, agent=None, draw_statics=False, agent_shape=None): ''' Args: screen (pygame.Surface) grid_mdp (MDP) state (State) show_value (bool) agent (Agent): Used to show value, by default uses VI. draw_statics (bool) agent_shape (pygame.rect) Returns: (pygame.Shape) ''' # Make value dict. val_text_dict = defaultdict(lambda: defaultdict(float)) if show_value: if agent is not None: # Use agent value estimates. for s in agent.q_func.keys(): val_text_dict[s.x][s.y] = agent.get_value(s) else: # Use Value Iteration to compute value. vi = ValueIteration(cleanup_mdp) vi.run_vi() for s in vi.get_states(): val_text_dict[s.x][s.y] = vi.get_value(s) # Make policy dict. policy_dict = defaultdict(lambda: defaultdict(str)) if policy: vi = ValueIteration(cleanup_mdp) vi.run_vi() for s in vi.get_states(): policy_dict[s.x][s.y] = policy(s) # Prep some dimensions to make drawing easier. scr_width, scr_height = screen.get_width(), screen.get_height() width_buffer = scr_width / 10.0 height_buffer = 30 + (scr_height / 10.0) # Add 30 for title. width = cleanup_mdp.width height = cleanup_mdp.height cell_width = (scr_width - width_buffer * 2) / width cell_height = (scr_height - height_buffer * 2) / height # goal_locs = grid_mdp.get_goal_locs() # lava_locs = grid_mdp.get_lavacc_locs() font_size = int(min(cell_width, cell_height) / 4.0) reg_font = pygame.font.SysFont("CMU Serif", font_size) cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2) # room_locs = [(x + 1, y + 1) for room in cleanup_mdp.rooms for (x, y) in room.points_in_room] door_locs = set([(door.x + 1, door.y + 1) for door in state.doors]) # Draw the static entities. # print(draw_statics) # draw_statics = True # if draw_statics: # For each row: for i in range(width): # For each column: for j in range(height): top_left_point = width_buffer + cell_width * i, height_buffer + cell_height * j r = pygame.draw.rect(screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3) # if policy and not grid_mdp.is_wall(i+1, height - j): if policy and (i + 1, height - j) in cleanup_mdp.legal_states: a = policy_dict[i + 1][height - j] if a not in action_char_dict: text_a = a else: text_a = action_char_dict[a] text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int( top_left_point[1] + cell_height / 3.0) text_rendered_a = cc_font.render(text_a, True, (46, 49, 49)) screen.blit(text_rendered_a, text_center_point) # if show_value and not grid_mdp.is_wall(i+1, grid_mdp.height - j): if show_value and (i + 1, height - j) in cleanup_mdp.legal_states: # Draw the value. val = val_text_dict[i + 1][height - j] color = mdpv.val_to_color(val) pygame.draw.rect(screen, color, top_left_point + (cell_width, cell_height), 0) # text_center_point = int(top_left_point[0] + cell_width/2.0 - 10), int(top_left_point[1] + cell_height/7.0) # text = str(round(val,2)) # text_rendered = reg_font.render(text, True, (46, 49, 49)) # screen.blit(text_rendered, text_center_point) # if grid_mdp.is_wall(i+1, grid_mdp.height - j): if (i + 1, height - j) not in cleanup_mdp.legal_states: # Draw the walls. top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5 pygame.draw.rect(screen, (94, 99, 99), top_left_point + (cell_width - 10, cell_height - 10), 0) if (i + 1, height - j) in door_locs: # Draw door # door_color = (66, 83, 244) door_color = (0, 0, 0) top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5 pygame.draw.rect(screen, door_color, top_left_point + (cell_width - 10, cell_height - 10), 0) else: room = cleanup_mdp.check_in_room(state.rooms, i + 1 - 1, height - j - 1) # Minus 1 for inconsistent x, y if room: top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5 room_rgb = _get_rgb(room.color) pygame.draw.rect(screen, room_rgb, top_left_point + (cell_width - 10, cell_height - 10), 0) block = cleanup_mdp.find_block(state.blocks, i + 1 - 1, height - j - 1) # print(state) # print(block) if block: circle_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) block_rgb = _get_rgb(block.color) pygame.draw.circle(screen, block_rgb, circle_center, int(min(cell_width, cell_height) / 4.0)) # Current state. if not show_value and (i + 1, height - j) == (state.x + 1, state.y + 1) and agent_shape is None: tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) agent_shape = _draw_agent(tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 8) if agent_shape is not None: # Clear the old shape. pygame.draw.rect(screen, (255, 255, 255), agent_shape) top_left_point = width_buffer + cell_width * ((state.x + 1) - 1), height_buffer + cell_height * ( height - (state.y + 1)) tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) # Draw new. # if not show_value or policy is not None: agent_shape = _draw_agent(tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 16) pygame.display.flip() return agent_shape
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()
def visualize_options_grid(grid_mdp, action_abstr, scr_width=720, scr_height=720): ''' Args: grid_mdp (GridWorldMDP) action_abstr (ActionAbstraction) ''' pygame.init() title_font = pygame.font.SysFont("CMU Serif", 32) small_font = pygame.font.SysFont("CMU Serif", 22) if len(action_abstr.get_actions()) == 0: print("Options Error: 0 options found. Can't visualize.") sys.exit(0) if isinstance(grid_mdp, MDPDistribution): goal_locs = set([]) for m in grid_mdp.get_all_mdps(): for g in m.get_goal_locs(): goal_locs.add(g) grid_mdp = grid_mdp.sample() else: goal_locs = grid_mdp.get_goal_locs() # Pygame init. screen = pygame.display.set_mode((scr_width, scr_height)) pygame.init() screen.fill((255, 255, 255)) pygame.display.update() mdp_visualizer._draw_title_text(grid_mdp, screen) option_text_point = scr_width / 2.0 - (14 * 7), 18 * scr_height / 20.0 # Setup states to compute option init/term funcs. state_dict = defaultdict(lambda: defaultdict(None)) vi = ValueIteration(grid_mdp) state_space = vi.get_states() for s in state_space: state_dict[s.x][s.y] = s # Draw inital option. option_index = 0 opt_str = "Option " + str(option_index + 1) + " of " + str( len(action_abstr.get_actions())) # + ":" + str(next_option) option_text = title_font.render(opt_str, True, (46, 49, 49)) screen.blit(option_text, option_text_point) next_option = action_abstr.get_actions()[option_index] visualize_option(screen, grid_mdp, state_dict, option=next_option) # Initiation rect and text. option_text = small_font.render("Init: ", True, (46, 49, 49)) screen.blit(option_text, (40, option_text_point[1])) pygame.draw.rect(screen, colors[0], (90, option_text_point[1]) + (24, 24)) # Terminal rect and text. option_text = small_font.render("Term: ", True, (46, 49, 49)) screen.blit(option_text, (scr_width - 150, option_text_point[1])) pygame.draw.rect(screen, colors[1], (scr_width - 80, option_text_point[1]) + (24, 24)) pygame.display.flip() # Keep updating options every space press. done = False while not done: # Check for key presses. for event in pygame.event.get(): if event.type == QUIT or (event.type == KEYDOWN and event.key == K_ESCAPE): # Quit. pygame.quit() sys.exit() if event.type == KEYDOWN and event.key == K_RIGHT: # Toggle to the next option. option_index = (option_index + 1) % len( action_abstr.get_actions()) elif event.type == KEYDOWN and event.key == K_LEFT: # Go to the previous option. option_index = (option_index - 1) % len( action_abstr.get_actions()) if option_index < 0: option_index = len(action_abstr.get_actions()) - 1 next_option = action_abstr.get_actions()[option_index] visualize_option(screen, grid_mdp, state_dict, option=next_option, goal_locs=goal_locs) pygame.draw.rect(screen, (255, 255, 255), (130, option_text_point[1]) + (scr_width - 290, 50)) opt_str = "Option " + str(option_index + 1) + " of " + str( len(action_abstr.get_actions())) # + ":" + str(next_option) option_text = title_font.render(opt_str, True, (46, 49, 49)) screen.blit(option_text, option_text_point)
def _draw_state(screen, grid_mdp, state, policy=None, action_char_dict={}, show_value=False, agent=None, draw_statics=False, agent_shape=None, options=[]): ''' Args: screen (pygame.Surface) grid_mdp (MDP) state (State) show_value (bool) agent (Agent): Used to show value, by default uses VI. draw_statics (bool) agent_shape (pygame.rect) Returns: (pygame.Shape) ''' # Make value dict. print('optinos=', options) val_text_dict = defaultdict(lambda: defaultdict(float)) if show_value: if agent is not None: # Use agent value estimates. for s in agent.q_func.keys(): val_text_dict[s.x][s.y] = agent.get_value(s) else: # Use Value Iteration to compute value. vi = ValueIteration(grid_mdp) vi.run_vi() for s in vi.get_states(): val_text_dict[s.x][s.y] = vi.get_value(s) # Make policy dict. policy_dict = defaultdict(lambda: defaultdict(str)) if policy: vi = ValueIteration(grid_mdp) vi.run_vi() for s in vi.get_states(): policy_dict[s.x][s.y] = policy(s) # Prep some dimensions to make drawing easier. scr_width, scr_height = screen.get_width(), screen.get_height() width_buffer = scr_width / 10.0 height_buffer = 30 + (scr_height / 10.0) # Add 30 for title. cell_width = (scr_width - width_buffer * 2) / grid_mdp.width cell_height = (scr_height - height_buffer * 2) / grid_mdp.height goal_locs = grid_mdp.get_goal_locs() lava_locs = grid_mdp.get_lava_locs() font_size = int(min(cell_width, cell_height) / 4.0) reg_font = pygame.font.SysFont("CMU Serif", font_size) cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2) # Draw the static entities. if draw_statics: # For each row: for i in range(grid_mdp.width): # For each column: for j in range(grid_mdp.height): top_left_point = width_buffer + cell_width * i, height_buffer + cell_height * j r = pygame.draw.rect( screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3) if policy and not grid_mdp.is_wall(i + 1, grid_mdp.height - j): a = policy_dict[i + 1][grid_mdp.height - j] if a not in action_char_dict: text_a = a else: text_a = action_char_dict[a] text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int(top_left_point[1] + cell_height / 3.0) text_rendered_a = cc_font.render(text_a, True, (46, 49, 49)) screen.blit(text_rendered_a, text_center_point) if show_value and not grid_mdp.is_wall(i + 1, grid_mdp.height - j): # Draw the value. val = val_text_dict[i + 1][grid_mdp.height - j] color = mdpv.val_to_color(val) pygame.draw.rect( screen, color, top_left_point + (cell_width, cell_height), 0) if grid_mdp.is_wall(i + 1, grid_mdp.height - j): # Draw the walls. top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5 r = pygame.draw.rect( screen, (94, 99, 99), top_left_point + (cell_width - 10, cell_height - 10), 0) if (i + 1, grid_mdp.height - j) in goal_locs: # Draw goal. # TODO: Better visualization? # circle_center = int(top_left_point[0] + cell_width/2.0), int(top_left_point[1] + cell_height/2.0) # circler_color = (154, 195, 157) # pygame.draw.circle(screen, circler_color, circle_center, int(min(cell_width, cell_height) / 3.0)) pass if (i + 1, grid_mdp.height - j) in lava_locs: # Draw goal. circle_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) circler_color = (224, 145, 157) pygame.draw.circle(screen, circler_color, circle_center, int(min(cell_width, cell_height) / 4.0)) # print('options') # print(i+1) # print(grid_mdp.height - j) # print(options) if (i + 1, j + 1) in options: # Draw options. # print('Needs to draw options at', i+1, '_', grid_mdp.height - j) #circle_center = int(top_left_point[0] + cell_width/2.0), int(top_left_point[1] + cell_height/2.0) #circler_color = (200, 200, 0) # #pygame.draw.circle(screen, circler_color, circle_center, int(min(cell_width, cell_height) / 4.0)) # Add a number for the option indices = [ k for k, x in enumerate(options) if x == (i + 1, j + 1) ] for index in indices: ind = int(index / 2) + 1 circle_center = int( top_left_point[0] + cell_width / 2.0) + int( cell_width / 6.0 * (ind + 1 - len(options) / 2)), int( top_left_point[1] + cell_height / 2.0) circler_color = (200, 200, 0) pygame.draw.circle( screen, circler_color, circle_center, int(min(cell_width, cell_height) / 4.0)) for index in indices: ind = int(index / 2) + 1 print('INDEX=', ind) font = pygame.font.SysFont(None, 24) text = font.render(str(ind), True, (0, 0, 0), (200, 200, 0)) textrect = text.get_rect() textrect.centerx = int( top_left_point[0] + cell_width / 2.0) + int( cell_width / 6.0 * (ind + 1 - len(options) / 2)) textrect.centery = int(top_left_point[1] + cell_height / 2.0) screen.blit(text, textrect) # Current state. # if not show_value and (i+1,grid_mdp.height - j) == (state.x, state.y) and agent_shape is None: # tri_center = int(top_left_point[0] + cell_width/2.0), int(top_left_point[1] + cell_height/2.0) # agent_shape = _draw_agent(tri_center, screen, base_size=min(cell_width, cell_height)/2.5 - 8) if agent_shape is not None: # Clear the old shape. pygame.draw.rect(screen, (255, 255, 255), agent_shape) top_left_point = width_buffer + cell_width * ( state.x - 1), height_buffer + cell_height * (grid_mdp.height - state.y) tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) # Draw new. agent_shape = _draw_agent( tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 8) pygame.display.flip() return agent_shape
def _draw_augmented_state(screen, taxi_oomdp, state, policy=None, action_char_dict={}, show_value=False, agent=None, draw_statics=True, agent_shape=None): ''' Args: screen (pygame.Surface) taxi_oomdp (TaxiOOMDP) state (State) agent_shape (pygame.rect) Returns: (pygame.Shape) ''' # There are multiple potential states for each grid cell (e.g. state also depends on whether the taxi currently has # the passenger or not), but the value and the policy for each cell is simply given by the most recent state # returned by get_states(). Began trying to display at least two values and optimal actions for each cell (depending # on the onboarding status of the passenger), but quickly realized that it gets very complicated as the MDP gets # more complicated (e.g. state also depends on the location of the passenger). # Displaying multiple values and optimal actions and will require either handcrafting the pipeline or # investing a lot of time into making the pipeline customizable and robust. Leaving incomplete attempt below as # commented out code. # Make value dict. val_text_dict = defaultdict(lambda: defaultdict(float)) # val_text_dict = defaultdict(lambda: defaultdict(lambda: defaultdict(float))) if show_value: if agent is not None: if agent.name == 'Q-learning': # Use agent value estimates. for s in agent.q_func.keys(): val_text_dict[s.get_agent_x()][ s.get_agent_y()] = agent.get_value(s) # val_text_dict[s.get_agent_x()][s.get_agent_y()][ # s.get_first_obj_of_class("passenger")["in_taxi"]] += agent.get_value(s) # slightly abusing the distinction between agents and planning modules... else: for s in taxi_oomdp.get_states(): val_text_dict[s.get_agent_x()][ s.get_agent_y()] = agent.get_value(s) # val_text_dict[s.get_agent_x()][s.get_agent_y()][ # s.get_first_obj_of_class("passenger")["in_taxi"]] += agent.get_value(s) else: # Use Value Iteration to compute value. vi = ValueIteration(taxi_oomdp, sample_rate=10) vi.run_vi() for s in vi.get_states(): val_text_dict[s.get_agent_x()][s.get_agent_y()] = vi.get_value( s) # val_text_dict[s.get_agent_x()][s.get_agent_y()][ # s.get_first_obj_of_class("passenger")["in_taxi"]] += vi.get_value(s) # Make policy dict. policy_dict = defaultdict(lambda: defaultdict(str)) # policy_dict = defaultdict(lambda: defaultdict(lambda : defaultdict(str))) if policy: for s in taxi_oomdp.get_states(): policy_dict[s.get_agent_x()][s.get_agent_y()] = policy(s) # if policy_dict[s.get_agent_x()][s.get_agent_y()][s.get_first_obj_of_class("passenger")["in_taxi"]] != '': # policy_dict[s.get_agent_x()][s.get_agent_y()][s.get_first_obj_of_class("passenger")["in_taxi"]] = policy(s) # Prep some dimensions to make drawing easier. scr_width, scr_height = screen.get_width(), screen.get_height() width_buffer = scr_width / 10.0 height_buffer = 30 + (scr_height / 10.0) # Add 30 for title. cell_width = (scr_width - width_buffer * 2) / taxi_oomdp.width cell_height = (scr_height - height_buffer * 2) / taxi_oomdp.height objects = state.get_objects() agent_x, agent_y = objects["agent"][0]["x"], objects["agent"][0]["y"] font_size = int(min(cell_width, cell_height) / 4.0) reg_font = pygame.font.SysFont("CMU Serif", font_size) cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2) if agent_shape is not None: # Clear the old shape. pygame.draw.rect(screen, (255, 255, 255), agent_shape) # Statics if draw_statics: # Draw walls. for w in taxi_oomdp.walls: w_x, w_y = w["x"], w["y"] top_left_point = width_buffer + cell_width * ( w_x - 1) + 5, height_buffer + cell_height * ( taxi_oomdp.height - w_y) + 5 pygame.draw.rect( screen, (46, 49, 49), top_left_point + (cell_width - 10, cell_height - 10), 0) # Draw tolls. for t in taxi_oomdp.tolls: t_x, t_y = t["x"], t["y"] top_left_point = width_buffer + cell_width * ( t_x - 1) + 5, height_buffer + cell_height * ( taxi_oomdp.height - t_y) + 5 # Clear the space and redraw with correct transparency (instead of simply adding a new layer which would # affect the transparency pygame.draw.rect( screen, (255, 255, 255), top_left_point + (cell_width - 10, cell_height - 10), 0) pygame.gfxdraw.box( screen, top_left_point + (cell_width - 10, cell_height - 10), (224, 230, 67)) # Draw traffic cells. for t in taxi_oomdp.traffic_cells: t_x, t_y = t["x"], t["y"] top_left_point = width_buffer + cell_width * ( t_x - 1) + 5, height_buffer + cell_height * ( taxi_oomdp.height - t_y) + 5 # Clear the space and redraw with correct transparency (instead of simply adding a new layer which would # affect the transparency pygame.draw.rect( screen, (255, 255, 255), top_left_point + (cell_width - 10, cell_height - 10), 0) pygame.gfxdraw.box( screen, top_left_point + (cell_width - 10, cell_height - 10), (58, 28, 232)) # Draw fuel stations. for f in taxi_oomdp.fuel_stations: f_x, f_y = f["x"], f["y"] top_left_point = width_buffer + cell_width * ( f_x - 1) + 5, height_buffer + cell_height * ( taxi_oomdp.height - f_y) + 5 pygame.draw.rect( screen, (144, 0, 255), top_left_point + (cell_width - 10, cell_height - 10), 0) # Draw the destination. for i, p in enumerate(objects["passenger"]): # Dest. dest_x, dest_y = p["dest_x"], p["dest_y"] top_left_point = int(width_buffer + cell_width * (dest_x - 1) + 27), int(height_buffer + cell_height * (taxi_oomdp.height - dest_y) + 14) dest_col = (int(max(color_ls[-i - 1][0] - 30, 0)), int(max(color_ls[-i - 1][1] - 30, 0)), int(max(color_ls[-i - 1][2] - 30, 0))) pygame.draw.rect(screen, dest_col, top_left_point + (cell_width / 6, cell_height / 6), 0) # Draw new agent. top_left_point = width_buffer + cell_width * ( agent_x - 1), height_buffer + cell_height * (taxi_oomdp.height - agent_y) agent_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) agent_shape = _draw_agent(agent_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 4) # Draw the passengers. for i, p in enumerate(objects["passenger"]): # Passenger pass_x, pass_y = p["x"], p["y"] taxi_size = int(min(cell_width, cell_height) / 9.0) if p["in_taxi"]: top_left_point = int(width_buffer + cell_width * (pass_x - 1) + taxi_size + 58), int(height_buffer + cell_height * (taxi_oomdp.height - pass_y) + taxi_size + 16) else: top_left_point = int(width_buffer + cell_width * (pass_x - 1) + taxi_size + 26), int(height_buffer + cell_height * (taxi_oomdp.height - pass_y) + taxi_size + 38) dest_col = (max(color_ls[-i - 1][0] - 30, 0), max(color_ls[-i - 1][1] - 30, 0), max(color_ls[-i - 1][2] - 30, 0)) pygame.draw.circle(screen, dest_col, top_left_point, taxi_size) if draw_statics: # For each row: for i in range(taxi_oomdp.width): # For each column: for j in range(taxi_oomdp.height): top_left_point = width_buffer + cell_width * i, height_buffer + cell_height * j r = pygame.draw.rect( screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3) # Show value of states. if show_value and not taxi_helpers.is_wall( taxi_oomdp, i + 1, taxi_oomdp.height - j): # Draw the value. val = val_text_dict[i + 1][taxi_oomdp.height - j] color = mdpv.val_to_color(val) pygame.draw.rect( screen, color, top_left_point + (cell_width, cell_height), 0) value_text = reg_font.render(str(round(val, 2)), True, (46, 49, 49)) text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int(top_left_point[1] + cell_height / 3.0) screen.blit(value_text, text_center_point) # Draw the value depending on the status of the passenger (incomplete) # val_1 = val_text_dict[s.get_agent_x()][s.get_agent_y()][0] # passenger not in taxi # val_2 = val_text_dict[s.get_agent_x()][s.get_agent_y()][1] # passenger not in taxi # color = mdpv.val_to_color((val_1 + val_2) / 2.) # pygame.draw.rect(screen, color, top_left_point + (cell_width, cell_height), 0) # # value_text = reg_font.render(str(round(val_1, 2)), True, (46, 49, 49)) # text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int( # top_left_point[1] + cell_height / 1.5) # screen.blit(value_text, text_center_point) # # value_text = reg_font.render(str(round(val_2, 2)), True, (46, 49, 49)) # text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int( # top_left_point[1] + cell_height / 4.5) # screen.blit(value_text, text_center_point) # Show optimal action to take in each grid cell. if policy and not taxi_helpers.is_wall(taxi_oomdp, i + 1, taxi_oomdp.height - j): a = policy_dict[i + 1][taxi_oomdp.height - j] if a not in action_char_dict: text_a = a else: text_a = action_char_dict[a] text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int(top_left_point[1] + cell_height / 3.0) text_rendered_a = cc_font.render(text_a, True, (46, 49, 49)) screen.blit(text_rendered_a, text_center_point) # Draw the policy depending on the status of the passenger (incomplete) # a_1 = policy_dict[i + 1][taxi_oomdp.height - j][0] # a_2 = policy_dict[i + 1][taxi_oomdp.height - j][0] # if a_1 not in action_char_dict: text_a_1 = a_1 # else: text_a_1 = action_char_dict[a_1] # if a_2 not in action_char_dict: text_a_2 = a_2 # else: text_a_2 = action_char_dict[a_2] # text_center_point = int(top_left_point[0] + cell_width/2.0 - 10), int(top_left_point[1] + cell_height/1.5) # text_rendered_a = cc_font.render(text_a_1, True, (46, 49, 49)) # screen.blit(text_rendered_a, text_center_point) # text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int( # top_left_point[1] + cell_height / 4.5) # text_rendered_a = cc_font.render(text_a_2, True, (46, 49, 49)) # screen.blit(text_rendered_a, text_center_point) pygame.display.flip() return agent_shape
def visualize_options_grid(grid_mdp, action_abstr, scr_width=720, scr_height=720): ''' Args: grid_mdp (GridWorldMDP) action_abstr (ActionAbstraction) ''' pygame.init() title_font = pygame.font.SysFont("CMU Serif", 32) small_font = pygame.font.SysFont("CMU Serif", 22) if len(action_abstr.get_actions()) == 0: print("Options Error: 0 options found. Can't visualize.") sys.exit(0) if isinstance(grid_mdp, MDPDistribution): goal_locs = set([]) for m in grid_mdp.get_all_mdps(): for g in m.get_goal_locs(): goal_locs.add(g) grid_mdp = grid_mdp.sample() else: goal_locs = grid_mdp.get_goal_locs() # Pygame init. screen = pygame.display.set_mode((scr_width, scr_height)) pygame.init() screen.fill((255, 255, 255)) pygame.display.update() mdp_visualizer._draw_title_text(grid_mdp, screen) option_text_point = scr_width / 2.0 - (14*7), 18*scr_height / 20.0 # Setup states to compute option init/term funcs. state_dict = defaultdict(lambda : defaultdict(None)) vi = ValueIteration(grid_mdp) state_space = vi.get_states() for s in state_space: state_dict[s.x][s.y] = s # Draw inital option. option_index = 0 opt_str = "Option " + str(option_index + 1) + " of " + str(len(action_abstr.get_actions())) # + ":" + str(next_option) option_text = title_font.render(opt_str, True, (46, 49, 49)) screen.blit(option_text, option_text_point) next_option = action_abstr.get_actions()[option_index] visualize_option(screen, grid_mdp, state_dict, option=next_option) # Initiation rect and text. option_text = small_font.render("Init: ", True, (46, 49, 49)) screen.blit(option_text, (40, option_text_point[1])) pygame.draw.rect(screen, colors[0], (90, option_text_point[1]) + (24, 24)) # Terminal rect and text. option_text = small_font.render("Term: ", True, (46, 49, 49)) screen.blit(option_text, (scr_width - 150, option_text_point[1])) pygame.draw.rect(screen, colors[1], (scr_width - 80, option_text_point[1]) + (24, 24)) pygame.display.flip() # Keep updating options every space press. done = False while not done: # Check for key presses. for event in pygame.event.get(): if event.type == QUIT or (event.type == KEYDOWN and event.key == K_ESCAPE): # Quit. pygame.quit() sys.exit() if event.type == KEYDOWN and event.key == K_RIGHT: # Toggle to the next option. option_index = (option_index + 1) % len(action_abstr.get_actions()) elif event.type == KEYDOWN and event.key == K_LEFT: # Go to the previous option. option_index = (option_index - 1) % len(action_abstr.get_actions()) if option_index < 0: option_index = len(action_abstr.get_actions()) - 1 next_option = action_abstr.get_actions()[option_index] visualize_option(screen, grid_mdp, state_dict, option=next_option, goal_locs=goal_locs) pygame.draw.rect(screen, (255, 255, 255), (130, option_text_point[1]) + (scr_width-290 , 50)) opt_str = "Option " + str(option_index + 1) + " of " + str(len(action_abstr.get_actions())) # + ":" + str(next_option) option_text = title_font.render(opt_str, True, (46, 49, 49)) screen.blit(option_text, option_text_point)
def old_draw_state(screen, cleanup_mdp, state, policy=None, action_char_dict={}, show_value=False, agent=None, draw_statics=False, agent_shape=None): ''' Args: screen (pygame.Surface) grid_mdp (MDP) state (State) show_value (bool) agent (Agent): Used to show value, by default uses VI. draw_statics (bool) agent_shape (pygame.rect) Returns: (pygame.Shape) ''' print('Inside draw state\n\n\n\n') # Make value dict. val_text_dict = defaultdict(lambda: defaultdict(float)) if show_value: if agent is not None: # Use agent value estimates. for s in agent.q_func.keys(): val_text_dict[s.x][s.y] = agent.get_value(s) else: # Use Value Iteration to compute value. vi = ValueIteration(cleanup_mdp) vi.run_vi() for s in vi.get_states(): val_text_dict[s.x][s.y] = vi.get_value(s) # Make policy dict. policy_dict = defaultdict(lambda: defaultdict(str)) if policy: vi = ValueIteration(cleanup_mdp) vi.run_vi() for s in vi.get_states(): policy_dict[s.x][s.y] = policy(s) # Prep some dimensions to make drawing easier. scr_width, scr_height = screen.get_width(), screen.get_height() width_buffer = scr_width / 10.0 height_buffer = 30 + (scr_height / 10.0) # Add 30 for title. width = cleanup_mdp.width height = cleanup_mdp.height cell_width = (scr_width - width_buffer * 2) / width cell_height = (scr_height - height_buffer * 2) / height # goal_locs = grid_mdp.get_goal_locs() # lava_locs = grid_mdp.get_lavacc_locs() font_size = int(min(cell_width, cell_height) / 4.0) reg_font = pygame.font.SysFont("CMU Serif", font_size) cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2) # room_locs = [(x + 1, y + 1) for room in cleanup_mdp.rooms for (x, y) in room.points_in_room] door_locs = set([(door.x + 1, door.y + 1) for door in state.doors]) # Draw the static entities. # print(draw_statics) # draw_statics = True # if draw_statics: # For each row: for i in range(width): # For each column: for j in range(height): top_left_point = width_buffer + cell_width * i, height_buffer + cell_height * j r = pygame.draw.rect(screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3) ''' # if policy and not grid_mdp.is_wall(i+1, height - j): if policy and (i + 1, height - j) in cleanup_mdp.legal_states: a = policy_dict[i + 1][height - j] if a not in action_char_dict: text_a = a else: text_a = action_char_dict[a] text_center_point = int(top_left_point[0] + cell_width / 2.0 - 10), int( top_left_point[1] + cell_height / 3.0) text_rendered_a = cc_font.render(text_a, True, (46, 49, 49)) screen.blit(text_rendered_a, text_center_point) # if show_value and not grid_mdp.is_wall(i+1, grid_mdp.height - j): if show_value and (i + 1, height - j) in cleanup_mdp.legal_states: # Draw the value. val = val_text_dict[i + 1][height - j] color = mdpv.val_to_color(val) pygame.draw.rect(screen, color, top_left_point + (cell_width, cell_height), 0) # text_center_point = int(top_left_point[0] + cell_width/2.0 - 10), int(top_left_point[1] + cell_height/7.0) # text = str(round(val,2)) # text_rendered = reg_font.render(text, True, (46, 49, 49)) # screen.blit(text_rendered, text_center_point) ''' # if grid_mdp.is_wall(i+1, grid_mdp.height - j): if (i + 1, height - j) not in cleanup_mdp.legal_states: # Draw the walls. top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5 pygame.draw.rect( screen, (94, 99, 99), top_left_point + (cell_width - 10, cell_height - 10), 0) if (i + 1, height - j) in door_locs: # Draw door # door_color = (66, 83, 244) door_color = (0, 0, 0) top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5 pygame.draw.rect( screen, door_color, top_left_point + (cell_width - 10, cell_height - 10), 0) else: room = cleanup_mdp.check_in_room( state.rooms, i + 1 - 1, height - j - 1) # Minus 1 for inconsistent x, y if room: top_left_point = width_buffer + cell_width * i + 5, height_buffer + cell_height * j + 5 room_rgb = _get_rgb(room.color) pygame.draw.rect( screen, room_rgb, top_left_point + (cell_width - 10, cell_height - 10), 0) block = cleanup_mdp.find_block(state.blocks, i + 1 - 1, height - j - 1) # print(state) # print(block) ''' # ROMA: to draw objects if needed if block: circle_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) block_rgb = _get_rgb(block.color) pygame.draw.circle(screen, block_rgb, circle_center, int(min(cell_width, cell_height) / 4.0)) # Current state. # ROMA: to draw the agent if needed if not show_value and (i + 1, height - j) == (state.x + 1, state.y + 1) and agent_shape is None: tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) agent_shape = _draw_agent(tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 8) ''' ''' if agent_shape is not None: # Clear the old shape. pygame.draw.rect(screen, (255, 255, 255), agent_shape) top_left_point = width_buffer + cell_width * ((state.x + 1) - 1), height_buffer + cell_height * ( height - (state.y + 1)) tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) # Draw new. # if not show_value or policy is not None: agent_shape = _draw_agent(tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 16) ''' pygame.display.flip() return agent_shape
def info_sa_planning_experiment(min_grid_size=5, max_grid_size=11, beta=10.0): ''' Args: min_grid_size (int) max_grid_size (int) beta (float): Hyperparameter for InfoSA. Summary: Writes num iterations and time (seconds) for planning with and without abstractions. ''' vanilla_file = "vi.csv" sa_file = "vi-$\\phi$.csv" file_prefix = os.path.join("results", "planning-four_room") clear_files(dir_name=file_prefix) for grid_dim in xrange(min_grid_size, max_grid_size + 1): # ====================== # == Make Environment == # ====================== mdp = FourRoomMDP(width=grid_dim, height=grid_dim, init_loc=(1, 1), goal_locs=[(grid_dim, grid_dim)], gamma=0.9) # Get demo policy. vi = ValueIteration(mdp) vi.run_vi() demo_policy = get_lambda_policy(make_det_policy_eps_greedy(vi.policy, vi.get_states(), mdp.get_actions(), epsilon=0.2)) # ======================= # == Make Abstractions == # ======================= pmf_s_phi, phi_pmf, abstr_policy = run_info_sa(mdp, demo_policy, iters=500, beta=beta, convergence_threshold=0.00001) lambda_abstr_policy = get_lambda_policy(abstr_policy) prob_s_phi = ProbStateAbstraction(phi_pmf) crisp_s_phi = convert_prob_sa_to_sa(prob_s_phi) # ============ # == Run VI == # ============ vanilla_vi = ValueIteration(mdp, delta=0.0001, sample_rate=25) sa_vi = AbstractValueIteration(ground_mdp=mdp, state_abstr=crisp_s_phi, delta=0.0001, vi_sample_rate=25, amdp_sample_rate=25) # ========== # == Plan == # ========== print "Running VIs." start_time = time.clock() vanilla_iters, vanilla_val = vanilla_vi.run_vi() vanilla_time = round(time.clock() - start_time, 2) mdp.reset() start_time = time.clock() sa_iters, sa_abs_val = sa_vi.run_vi() sa_time = round(time.clock() - start_time, 2) sa_val = evaluate_agent(FixedPolicyAgent(sa_vi.policy), mdp, instances=25) print "\n" + "*"*20 print "Vanilla", "\n\t Iters:", vanilla_iters, "\n\t Value:", round(vanilla_val, 4), "\n\t Time:", vanilla_time print print "Phi:", "\n\t Iters:", sa_iters, "\n\t Value:", round(sa_val, 4), "\n\t Time:", sa_time print "*"*20 + "\n\n" write_datum(os.path.join(file_prefix, "iters", vanilla_file), vanilla_iters) write_datum(os.path.join(file_prefix, "iters", sa_file), sa_iters) write_datum(os.path.join(file_prefix, "times", vanilla_file), vanilla_time) write_datum(os.path.join(file_prefix, "times", sa_file), sa_time)
def _draw_state(screen, pudd_mdp, state, policy=None, action_char_dict={}, show_value=False, agent=None, draw_statics=False, agent_shape=None): ''' Args: screen (pygame.Surface) pudd_mdp (MDP) state (State) show_value (bool) agent (Agent): Used to show value, by default uses VI. draw_statics (bool) agent_shape (pygame.rect) Returns: (pygame.Shape) ''' # Make value dict. val_text_dict = defaultdict(lambda: defaultdict(float)) if show_value: if agent is not None: # Use agent value estimates. for s in agent.q_func.keys(): val_text_dict[s.x][s.y] = agent.get_value(s) else: # Use Value Iteration to compute value. vi = ValueIteration(pudd_mdp) vi.run_vi() for s in vi.get_states(): val_text_dict[s.x][s.y] = vi.get_value(s) # Make policy dict. policy_dict = defaultdict(lambda: defaultdict(str)) if policy: vi = ValueIteration(pudd_mdp) vi.run_vi() for s in vi.get_states(): policy_dict[s.x][s.y] = policy(s) # Prep some dimensions to make drawing easier. scr_width, scr_height = screen.get_width(), screen.get_height() width_buffer = scr_width / 10.0 height_buffer = 30 + (scr_height / 10.0) # Add 30 for title. cell_width = (scr_width - width_buffer * 2) / 10 #pudd_mdp.width cell_height = (scr_height - height_buffer * 2) / 10 # pudd_mdp.height goal_locs = pudd_mdp.get_goal_locs() # puddle_rects = pudd_mdp.get_puddle_rects() font_size = int(min(cell_width, cell_height) / 4.0) reg_font = pygame.font.SysFont("CMU Serif", font_size) cc_font = pygame.font.SysFont("Courier", font_size * 2 + 2) delta = 0.2 #print ("goal locs", goal_locs) # Draw the static entities. if draw_statics: # For each row: for i in np.linspace(0, 1, 11): # For each column: for j in np.linspace(0, 1, 11): #print ("i,j",i,j) top_left_point = width_buffer + cell_width * i * 10, height_buffer + cell_height * j * 10 r = pygame.draw.rect( screen, (46, 49, 49), top_left_point + (cell_width, cell_height), 3) if pudd_mdp.is_puddle_location(i, j): # Draw the walls. #print ("True for ", i, j) top_left_point = width_buffer + cell_width * ( i * 10) + 5, height_buffer + cell_height * j * 10 + 5 r = pygame.draw.rect( screen, (0, 127, 255), top_left_point + (cell_width - 10, cell_height - 10), 0) if [min(i + delta, 1.0), min(j + delta, 1.0)] in goal_locs: # Draw goal. circle_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) circler_color = (154, 195, 157) pygame.draw.circle(screen, circler_color, circle_center, int(min(cell_width, cell_height) / 3.0)) # Current state. if not show_value and (round(i, 1), round(j, 1)) == (round( state.x, 1), round(state.y, 1)) and agent_shape is None: tri_center = int(top_left_point[0] + cell_width / 2.0), int(top_left_point[1] + cell_height / 2.0) agent_shape = _draw_agent( tri_center, screen, base_size=min(cell_width, cell_height) / 2.5 - 8) # if agent_shape is not None: # # Clear the old shape. # pygame.draw.rect(screen, (255,255,255), agent_shape) # top_left_point = width_buffer + cell_width*(state.x - 1), height_buffer + cell_height*(pudd_mdp.height - state.y) # tri_center = int(top_left_point[0] + cell_width/2.0), int(top_left_point[1] + cell_height/2.0) # Draw new. # agent_shape = _draw_agent(tri_center, screen, base_size=min(cell_width, cell_height)/2.5 - 8) pygame.display.flip() return agent_shape
def make_abstr_mdp(mdp, state_abstr, action_abstr, sample_rate=25): ''' Args: mdp (MDP) state_abstr (StateAbstraction) action_abstr (ActionAbstraction) sample_rate (int): Sample rate for computing the abstract R and T. Returns: (MDP) ''' # Grab ground state space. vi = ValueIteration(mdp) state_space = vi.get_states() # Make abstract reward and transition functions. def abstr_reward_lambda(abstr_state, abstr_action): # Get relevant MDP components from the lower MDP. lower_states = state_abstr.get_lower_states_in_abs_state(abstr_state) lower_reward_func = mdp.get_reward_func() lower_trans_func = mdp.get_transition_func() # Compute reward. total_reward = 0 for ground_s in lower_states: for sample in xrange(sample_rate): s_prime, reward = abstr_action.rollout(ground_s, lower_reward_func, lower_trans_func) total_reward += float(reward) / ( len(lower_states) * sample_rate) # Add weighted reward. # print "~"*20 # print "R_A:", abstr_state, abstr_action, total_reward # print "~"*20 return total_reward def abstr_transition_lambda(abstr_state, abstr_action): # print "Abstr Transition Func:" # print "\t abstr_state:", abstr_state # Get relevant MDP components from the lower MDP. lower_states = state_abstr.get_lower_states_in_abs_state(abstr_state) lower_reward_func = mdp.get_reward_func() lower_trans_func = mdp.get_transition_func() # Compute next state distribution. s_prime_prob_dict = defaultdict(int) total_reward = 0 for ground_s in lower_states: for sample in xrange(sample_rate): s_prime, reward = abstr_action.rollout(ground_s, lower_reward_func, lower_trans_func) s_prime_prob_dict[s_prime] += ( 1.0 / (len(lower_states) * sample_rate) ) # Weighted average. # Form distribution and sample s_prime. end_ground_state = s_prime_prob_dict.keys()[list( np.random.multinomial( 1, s_prime_prob_dict.values()).tolist()).index(1)] end_abstr_state = state_abstr.phi(end_ground_state, level=abstr_state.get_level()) return end_abstr_state # Make the components of the MDP. abstr_init_state = state_abstr.phi(mdp.get_init_state()) abstr_action_space = action_abstr.get_actions() abstr_state_space = state_abstr.get_abs_states() abstr_reward_func = RewardFunc(abstr_reward_lambda, abstr_state_space, abstr_action_space) abstr_transition_func = TransitionFunc(abstr_transition_lambda, abstr_state_space, abstr_action_space, sample_rate=sample_rate) # Make the MDP. abstr_mdp = MDP(actions=abstr_action_space, init_state=abstr_init_state, reward_func=abstr_reward_func.reward_func, transition_func=abstr_transition_func.transition_func, gamma=0.5) return abstr_mdp
def make_policy_blocks_options(mdp_distr, num_options, task_samples): ''' Args: mdp_distr (MDPDistribution) num_options (int) task_samples (int) Returns: (list): Contains policy blocks options. ''' option_set = [] # Fill solution set for task_samples draws from MDP distribution L = [] for new_task in xrange(task_samples): print " Sample " + str(new_task + 1) + " of " + str(task_samples) + "." # Sample the MDP. mdp = mdp_distr.sample() # Run VI to get a policy for the MDP as well as the list of states print "\tRunning VI...", sys.stdout.flush() # Run VI vi = ValueIteration(mdp, delta=0.0001, max_iterations=5000, sample_rate=5) iters, val = vi.run_vi() print " done." policy = make_dict_from_lambda(vi.policy, vi.get_states()) L.append(policy) power_L = get_power_set(L) num_iters = 1 print 'Beginning policy blocks for {2} options with {0} solution policies and power set of size {1}'\ .format(len(L), len(power_L), num_options) while len(power_L) > 0 and len(option_set) < num_options: print 'Running iteration {0} of policy blocks...'.format(num_iters) # Initialize empty set of candidate option policies C = [] # Iterate over the power set of solution policies for policy_set in power_L: # Compute candidate policy as merge over policy set candidate = policy_blocks_merge(policy_set) if candidate not in C: # Compute score of each candidate policy C.append((candidate, policy_blocks_score_policy(candidate, L))) # Identify the candidate policy with highest score and add to option set C = sorted(C, key=lambda x: x[1]) pi_star = C[-1][0] if pi_star not in option_set: option_set.append(pi_star) # Subtract chosen candidate from L by iterating through power set power_L = map(lambda policy_set: [policy_blocks_subtract_pair(p, pi_star) for p in policy_set], power_L) # Remove empty elements of power set power_L = filter(lambda policy_set: sum(map(lambda x: len(x), policy_set)) > 0, power_L) num_iters += 1 # Generate true option set ret = [] for o in option_set: init_predicate = CovPredicate(y=True, policy=o) term_predicate = CovPredicate(y=False, policy=o) print map(str, o.keys()) print o.values() print '**' opt = Option(init_predicate=init_predicate, term_predicate=term_predicate, policy=o) ret.append(opt) print 'Policy blocks returning with {0} options'.format(len(ret)) return ret