goal=(3, 2), step_cost=1, walls=[(1, 1), (2, 2)]) algo = RAOStar(model, cc=0.1, debugging=False, cc_type='o', fixed_horizon=6, random_node_selection=False) policy_current_belief = { (1, 0): 1.0 } # initial state is: current vehicle s_time = time.time() P, G = algo.search(policy_current_belief) print('expanded nodes', len(G.nodes)) print('objective:', G.root.value) print('Time:', time.time() - s_time) IPython.embed() elif Network: model = NetworkModel() algo = RAOStar(model, cc=0.20, debugging=False, cc_type='o', fixed_horizon=6, random_node_selection=True)
if len(sys.argv) > 1: cc = float(sys.argv[1]) model = Ashkan_ICAPS_Model(str(cc * 100) + "% risk") algo = RAOStar(model, cc=cc, debugging=False, cc_type='e', ashkan_continuous=True) agent_coords = [6, 9] agent_coords = [7, 3] b0 = ContinuousBeliefState(9, 1) b0.set_agent_coords(agent_coords[0], agent_coords[1]) P, G = algo.search(b0) most_likely_policy(G, model) Sim = Simulator(10, 10, G, P, model, grid_size=50) # Convert all matrices to strings for json # print(G) gshow = graph_to_json.policy_to_json(G, 0.5, 'results/ashkan_9_1.json') # code to draw the risk grid # for i in range(11): # for j in range(11): # static_risk = static_obs_risk_coords( # np.matrix([i, j]), np.matrix([[0.2, 0], [0, 0.2]])) # dynamic_risk = dynamic_obs_risk_coords(
from iterative_raostar import * #### Run RAO star on Scenario #### # Simulation conditions world_size = (7, 7) # note the boundaries are walls goal_state = (5, 5, 90) quad_init = (1, 1, 90, 0) # (x,y,theta,t) guest_init = (3, 1, 90, 0) # note the boundary of the world (ex anything with row column 0 and the upper bound) # is the wall model = QuadModel(world_size, goal_state) algo = RAOStar(model, cc=0.5, debugging=False) b_init = {(quad_init, guest_init): 1.0} # initialize belief state P, G = algo.search(b_init) # # get the policies that does not give none # P_notNone = {} # for i in P: # if P[i] != 'None': # P_notNone[i] = P[i] # print(P_notNone) # # print out the policy for each state of guest # most_likely_policy(G, model) gshow = graph_to_json.policy_to_json(G, 0.5, 'quadraos.json')
print(agent1_vehicle.action_list[0].precondition_check( agent1_vehicle.name, geordi_model.current_state, geordi_model)) new_states = geordi_model.state_transitions(geordi_model.current_state, actions[0]) print('new_states', new_states) algo = RAOStar(geordi_model, cc=0.01, debugging=False, cc_type='o', fixed_horizon=1) b_init = {geordi_model.current_state: 1.0} P, G = algo.search(b_init, iter_limit=5) for keys, values in P.items(): state, probability, depth = keys best_action = values node_info = {} node_info['state'] = state node_info['probability'] = probability node_info['depth'] = depth node_info['the_best_action'] = best_action # if depth == 0: # if best_action == 'ActionModel(ego_forward)': # result = 'forward' # elif best_action == 'ActionModel(ego_merge_right)':