def evaluate(index):
game = CartPole()
actions = game.legal_actions
dqn = DQN(actions)
dqn.epsilon = 0
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver()
checkpoint = tf.train.get_checkpoint_state("networks")
if checkpoint:
saver.restore(sess, checkpoint.all_model_checkpoint_paths[index])
print "Loaded: %s" % checkpoint.all_model_checkpoint_paths[index]
rewards = []
for episode in range(200):
state = game.newGame()
totReward = 0
for _ in range(400):
if episode == 199:
game.env.render()
action = dqn.selectAction(state)
actionNum = np.argmax(action)
next_state, reward, game_over = game.next(actionNum)
totReward += reward
state = next_state
if game_over:
break
rewards.append(totReward)
print rewards
print "Average %s, best %s" % (sum(rewards) / len(rewards), max(rewards))
def train():
game = CartPole()
actions = game.legal_actions
dqn = DQN(actions)
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver()
state = game.newGame()
for episode in range(T):
action = dqn.selectAction(state)
actionNum = np.argmax(action)
game.env.render()
next_state, reward, game_over = game.next(actionNum)
if game_over:
dqn.storeExperience(state, action, 0, next_state, game_over)
next_state = game.newGame()
else:
dqn.storeExperience(state, action, reward, next_state, game_over)
##TODO: sample a minibatch from the replay buffer
state_batch = ???
nextState_batch =???
action_batch =???
terminal_batch =???
reward_batch =???
y_batch = []
Q_batch = sess.run(dqn.targetQNet.QValue, feed_dict = {dqn.targetQNet.stateInput: nextState_batch} )
for i in range(len(minibatch)):
terminal = terminal_batch[i]
if terminal:
y_batch.append(reward_batch[i])
else:
## TODO: add the target to the list of targets for each element in the minibatch using Q update rule
y_batch.append(???)
currentQ_batch = sess.run(dqn.currentQNet.QValue,
feed_dict = {dqn.currentQNet.stateInput: state_batch })
sess.run(dqn.trainStep, feed_dict = {dqn.yInput: y_batch, dqn.actionInput: action_batch, dqn.currentQNet.stateInput: state_batch})
state = next_state
if episode % UPDATE_TIME == 0:
sess.run(dqn.copyCurrentToTargetOperation())
if episode % 25000 == 0:
saver.save(sess, 'networks/' + 'dqn', global_step= episode)
if dqn.epsilon > FINAL_EPSILON:
## TODO: decay epsilon which represents the probability of taking a random action
dqn.epsilon -=