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
