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, temperature=TEMPERATURE, delta_t=TEMPERATURE / ITERATIONS) agent.Q = Q3()
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, temperature=TEMPERATURE, delta_t=TEMPERATURE / ITERATIONS