def search_for_one_solution(problem_id, map_name, plot_or_not):
problem_id = problem_id
reward_hole = 0.0
is_stochastic = False
if map_name == '4x4-base':
n_dim = 4
else:
n_dim = 8
env = LochLomondEnv(problem_id = problem_id, is_stochastic = is_stochastic, reward_hole = reward_hole, map_name_base = map_name)
env.reset()
# Create a dict representation of the state space
state_space_locations, state_space_actions, state_initial_id, state_goal_id = env2statespace(env)
#--------------SOLUTION--------------#
maze_map = UndirectedGraph(state_space_actions)
maze_map.locations = state_space_locations
maze_problem = GraphProblem(state_initial_id, state_goal_id, maze_map)
iterations, _, node = my_astar_search_graph(problem=maze_problem, h=None)
#-------------Trace the solution-----------------#
solution_path = [node]
cnode = node.parent
solution_path.append(cnode)
i = 0
while cnode.state != state_initial_id:
i += 1
cnode = cnode.parent
solution_path.append(cnode)
solution = []
solution_x = []
solution_y = []
for s in str(solution_path).split('_',-1):
for s_s in str(s).split('>',-1):
if s_s.isdigit():
solution.append(s_s)
for i in range(int(len(solution)/2)):
solution_y.append(int(solution[i*2]))
solution_x.append(int(solution[i*2+1]))
print("Steps:",i)
print("Goal state:"+str(solution_path[0]))
print("Final Solution:",solution_path[::-1])
print("----------------------------------------")
env.close()
plt.cla()
plt.plot(solution_x[::-1], solution_y[::-1])
plt.scatter(solution_x[::-1], solution_y[::-1],s=120)
plt.xlim(0,n_dim-1)
plt.ylim(n_dim-1,0)
plt.grid(True)
plt.title("Simple Agent Solution for Problem%s" % problem_id)
plt.savefig('./Images/%sx%s maps: Simple Agent Solution for Problem%s.jpg' % (n_dim,n_dim,problem_id))
print("Figure Saved in Folder 'Images'")
if plot_or_not == True:
plt.show()
def train_for_one_problem(problem_id, map_name):
problem_id = problem_id # problem_id \in [0:7] generates 8 diffrent problems on which you can train/fine-tune your agent
reward_hole = 0.0 # should be less than or equal to 0.0 (you can fine tune this depending on you RL agent choice)
is_stochastic = False # should be False for A-star (deterministic search) and True for the RL agent
env = LochLomondEnv(problem_id=problem_id,
is_stochastic=is_stochastic,
reward_hole=reward_hole,
map_name_base=map_name)
env.reset()
done = False
total_test_num = 60000
restart_times = 0
succeed_times = 0
shortest_path = 100
one_map_succeed_percentage = []
for i in range(total_test_num):
restart_times += 1
done = False
n_actions_for_episode = 0
while not done:
n_actions_for_episode += 1
action = env.action_space.sample(
) # take random action from the available actions
observation, reward, done, info = env.step(action)
if done:
print("\rProblem:%s Episodes #%s / 60000" %
(problem_id, restart_times),
end='')
if reward == 1.0:
if shortest_path > n_actions_for_episode:
shortest_path = n_actions_for_episode
succeed_times += 1
else:
env.reset()
print("\nSucceed Times:", succeed_times)
print("Total Times:", total_test_num)
print("Shortest path:", shortest_path)
one_map_succeed_percentage = float(succeed_times / 60000)
return one_map_succeed_percentage
env.close()