[英]Deep Q Network not Solving OpenAI CartPole
我是強化學習的初學者,正嘗試實施DQN以解決OpenAI Gym中的CartPole-v0任務。 不幸的是,我的實現的性能似乎並沒有提高。
當前,隨着訓練的進行,情節獎勵實際上減少了,而目標是找到更好的策略來增加這一價值。
我正在使用經驗重播和單獨的目標網絡來備份我的q值。 我嘗試在代理中添加/刪除層和神經元; 這沒有用。 我改變了降低勘探速度的時間表。 這也不起作用。 我越來越相信損失函數有問題,但是我不確定如何更改它以提高性能。
這是我的損失函數代碼:
with tf.variable_scope('loss'):
one_hot_mask = self.one_hot_actions
eval = tf.reduce_max(self.q * one_hot_mask, axis=1)
print(eval)
trg = tf.reduce_max(self.q_targ, axis = 1) * self.gamma
print(trg)
label = trg + self.rewards
self.loss = tf.reduce_mean(tf.square(label - eval))
其中one_hot_actions是占位符,定義為:
self.one_hot_actions = tf.placeholder(tf.float32, [None, self.env.action_space.n], 'one_hot_actions')
任何幫助是極大的贊賞。 這是我的完整代碼:
import tensorflow as tf
import numpy as np
import gym
import sys
import random
import math
import matplotlib.pyplot as plt
class Experience(object):
"""Experience buffer for experience replay"""
def __init__(self, size):
super(Experience, self).__init__()
self.size = size
self.memory = []
def add(self, sample):
self.memory.append(sample)
if len(self.memory) > self.size:
self.memory.pop(0)
class Agent(object):
def __init__(self, env, ep_max, ep_len, gamma, lr, batch, epochs, s_dim, minibatch_size):
super(Agent, self).__init__()
self.ep_max = ep_max
self.ep_len = ep_len
self.gamma = gamma
self.experience = Experience(100)
self.lr = lr
self.batch = batch
self.minibatch_size = minibatch_size
self.epochs = epochs
self.s_dim = s_dim
self.sess = tf.Session()
self.env = gym.make(env).unwrapped
self.state_0s = tf.placeholder(tf.float32, [None, self.s_dim], 'state_0s')
self.actions = tf.placeholder(tf.int32, [None, 1], 'actions')
self.rewards = tf.placeholder(tf.float32, [None, 1], 'rewards')
self.states = tf.placeholder(tf.float32, [None, self.s_dim], 'states')
self.one_hot_actions = tf.placeholder(tf.float32, [None, self.env.action_space.n], 'one_hot_actions')
# q nets
self.q, q_params = self.build_dqn('primary', trainable=True)
self.q_targ, q_targ_params = self.build_dqn('target', trainable=False)
with tf.variable_scope('update_target'):
self.update_target_op = [targ_p.assign(p) for p, targ_p in zip(q_params, q_targ_params)]
with tf.variable_scope('loss'):
one_hot_mask = self.one_hot_actions
eval = tf.reduce_max(self.q * one_hot_mask, axis=1)
print(eval)
trg = tf.reduce_max(self.q_targ, axis = 1) * self.gamma
print(trg)
label = trg + self.rewards
self.loss = tf.reduce_mean(tf.square(label - eval))
with tf.variable_scope('train'):
self.train_op = tf.train.AdamOptimizer(self.lr).minimize(self.loss)
tf.summary.FileWriter("log/", self.sess.graph)
self.sess.run(tf.global_variables_initializer())
def build_dqn(self, name, trainable):
with tf.variable_scope(name):
if name == "primary":
l1 = tf.layers.dense(self.state_0s, 100, tf.nn.relu, trainable=trainable)
else:
l1 = tf.layers.dense(self.states, 100, tf.nn.relu, trainable=trainable)
l2 = tf.layers.dense(l1, 50, tf.nn.relu, trainable=trainable)
q = tf.layers.dense(l2, self.env.action_space.n, trainable=trainable)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
return q, params
def choose_action(self, s, t):
s = s[np.newaxis, :]
if random.uniform(0,1) < self.get_explore_rate(t):
a = self.env.action_space.sample()
else:
a = np.argmax(self.sess.run(self.q, {self.state_0s: s})[0])
return a
def get_explore_rate(self, t):
return max(0.01, min(1, 1.0 - math.log10((t+1)/25)))
def update(self):
# experience is [ [s_0, a, r, s], [s_0, a, r, s], ... ]
self.sess.run(self.update_target_op)
indices = np.random.choice(range(len(self.experience.memory)), self.batch)
# indices = range(len(experience))
state_0 = [self.experience.memory[index][0] for index in indices]
a = [self.experience.memory[index][1] for index in indices]
rs = [self.experience.memory[index][2] for index in indices]
state = [self.experience.memory[index][3] for index in indices]
[self.sess.run(self.train_op, feed_dict = {self.state_0s: state_0,
self.one_hot_actions: a, self.rewards: np.asarray(rs).reshape([-1,1]), self.states: state}) for _ in range(self.epochs)]
def run(self):
all_ep_r = []
for ep in range(self.ep_max):
s_0 = self.env.reset()
ep_r = 0
for t in range(self.ep_len):
fake_ac = [0.0, 0.0] # used to make one hot actions
# self.env.render()
a = self.choose_action(s_0, ep)
s, r, done, _ = self.env.step(a)
if done:
s = np.zeros(np.shape(s_0))
fake_ac[a] = 1.0
print(fake_ac)
self.experience.add([s_0, fake_ac, r, s])
s_0 = s
ep_r += r
if done:
break
all_ep_r.append(ep_r)
print(
'Ep: %i' % ep,
"|Ep_r: %i" % ep_r,
)
if len(self.experience.memory) > self.batch -1:
self.update()
return all_ep_r
agent = Agent("CartPole-v0", 200, 200, 0.99, 0.00025, 32, 10, 4, 16)
all_ep_r = agent.run()
plt.plot(range(len(all_ep_r)), all_ep_r)
plt.show()
西蒙的評論是正確的。 您丟失函數的代碼不正確,因為您沒有考慮終端狀態。
當且僅當狀態為非終結狀態時,目標trg
應為reward + gamma * Q
。
如果狀態為末期(桿位下降而游戲結束),則僅是reward
。
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