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)':