ITERATIONS = 5000 TEMPERATURE = 1.0 N = 1 if __name__ == '__main__': times = {2: [], 3: []} flatland = flatland_from_file('../scenarios/5-even-bigger.txt') backup_x = int(sqrt(manhattan_distance((0, 0), (flatland.w, flatland.h)))) for i in xrange(N): print(2, i) agent = FlatlandAgent(world=deepcopy(flatland), step_limit=flatland.w * flatland.h, backup_x=backup_x, temperature=TEMPERATURE, delta_t=TEMPERATURE / ITERATIONS) agent.Q = Q2() start = time() agent.train() finish = time() times[2].append(finish - start) for i in xrange(N): print(3, i) agent = FlatlandAgent(world=deepcopy(flatland), step_limit=flatland.w * flatland.h, backup_x=backup_x,
N = 1 if __name__ == '__main__': times = { 2: [], 3: [] } flatland = flatland_from_file('../scenarios/5-even-bigger.txt') backup_x = int(sqrt(manhattan_distance((0, 0), (flatland.w, flatland.h)))) for i in xrange(N): print(2, i) agent = FlatlandAgent( world=deepcopy(flatland), step_limit=flatland.w * flatland.h, backup_x=backup_x, temperature=TEMPERATURE, delta_t=TEMPERATURE / ITERATIONS ) agent.Q = Q2() start = time() agent.train() finish = time() times[2].append(finish - start) for i in xrange(N): print(3, i) agent = FlatlandAgent( world=deepcopy(flatland),
type=int, default=None, help='Defaults to a value proportionate to the world size') parser.add_argument('--plot', action='store_true') args = parser.parse_args() flatland = flatland_from_file(args.scenario) backup_x = args.backup if args.backup is None: backup_x = int( sqrt(manhattan_distance((0, 0), (flatland.w, flatland.h)))) agent = FlatlandAgent(world=flatland, step_limit=flatland.w * flatland.h, backup_x=backup_x, temperature=args.temperature, delta_t=args.temperature / args.iterations) ideal_temp = [] experienced_temp = [] start = time() def after(): e = agent.explore / agent.steps ideal_temp.append(agent.temperature) experienced_temp.append(e) print(round(agent.temperature, 3), round(e, 3), agent.steps,
parser.add_argument( '--plot', action='store_true' ) args = parser.parse_args() flatland = flatland_from_file(args.scenario) backup_x = args.backup if args.backup is None: backup_x = int(sqrt(manhattan_distance((0, 0), (flatland.w, flatland.h)))) agent = FlatlandAgent( world=flatland, step_limit=flatland.w * flatland.h, backup_x=backup_x, temperature=args.temperature, delta_t=args.temperature / args.iterations ) ideal_temp = [] experienced_temp = [] start = time() def after(): e = agent.explore / agent.steps ideal_temp.append(agent.temperature) experienced_temp.append(e)