def main(argv):
cmd, mazefile, inpathfile, outpathfile, qlconfig = parseArgv(argv)
world = environment.GridWorld(Cell, None, None, mazefile)
if (cmd == 'astar'):
# TOTO write a search agent to handle all this
if not inpathfile:
problem = search.ProblemImpl(world);
s = planner.search.AStar(problem)
s.search()
path = s.getPath()
results = s.getResults()
if outpathfile:
path = s.savePath(outpathfile, path)
else:
path = loadPath(inpathfile)
results = {}
vi = Visualization(world)
vi.setPath(path)
vi.setResults(results)
vi.animatePath = True
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit(0)
vi.update()
elif (cmd == 'qlearn'):
problem = learning.RLProblemImpl(world)
actions = problem.actions(13)
policy = qlearning.EpsilonGreedyPolicy(0.05)
ai = qlearning.QLearn(problem, policy, qlconfig["alpha"], qlconfig["gamma"])
# place agent in random position
npc = learning.LearnAgent('@', getRandomLocation(problem), random.randrange(4), ai)
world.addAgent(npc)
steps = 150
for x in range(qlconfig["episodes"]):
for step in range(steps):
world.update()
if world.isDone() == True:
break
loc = getRandomLocation(problem)
npc.setLocation(loc)
if True:
print "Exploit:\n"
ai.trace = True
policy.epsilon = 0.0
for x in range(2):
print "Episode= %d" % (x+1)
for step in range(steps):
world.update()
if world.isDone() == True:
break;
print "Number of steps= %d" % (step+1)
loc = getRandomLocation(problem)
npc.setLocation(loc)
npc._ai.viResults()
