# Play a game
while not done:
if random.random() < exploration_rate:
action = random.randint(0, 1)
else:
actions = mlp.frontprop(normalize(np.array(state)))
action = np.argmax(actions)
# action = np.argmin(actions)
short_mem.append((state, action))
state, reward, done, _ = env.step(action)
if render: env.render()
score += 1
# If win then learn short mem
if score == 500:
for state, action in short_mem:
mlp.backprop(normalize(np.array(state)),
np.array([1, 0] if action == 0 else [0, 1]))
mlp.fit()
# long_mem.append((state, action))
# If loose, don't
else:
pop = True
while pop:
state, action = short_mem.pop()
actions = mlp.frontprop(normalize(np.array(state)))
# Action chosen is bad
if action == 0:
expected = np.array([0, actions[1]])
else:
expected = np.array([actions[0], 0])
# Learn corrected action
mlp.backprop(normalize(np.array(state)), expected)
