【陪你聊TensorLayer

陪你聊TensorLayer"/>

【陪你聊TensorLayer

源码地址：.py

以下本人结合《深度学习：一起玩转Tensorlayer》一书进行中文注释，大牛就不必看了，面向初次接触深度强化学习与Tensorlayer深度学习高级AI库的新手。之后会有补充。

#! /usr/bin/python
# -*- coding: utf-8 -*-
""" Monte-Carlo Policy Network π(a|s)  (REINFORCE).
To understand Reinforcement Learning, we let computer to learn how to play
Pong game from the original screen inputs. Before we start, we highly recommend
you to go through a famous blog called “Deep Reinforcement Learning: Pong from
Pixels” which is a minimalistic implementation of deep reinforcement learning by
using python-numpy and OpenAI gym environment.
The code here is the reimplementation of Karpathy's Blog by using TensorLayer.
Compare with Karpathy's code, we store observation for a batch, he store
observation for a episode only, they store gradients instead. (so we will use
more memory if the observation is very large.)
Link
-----
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"""

import time
import gym
import numpy as np
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import DenseLayer, InputLayertf.logging.set_verbosity(tf.logging.DEBUG)
tl.logging.set_verbosity(tl.logging.DEBUG)# hyper-parameters 超参数设置
image_size = 80
D = image_size * image_size
H = 200
batch_size = 10
learning_rate = 1e-4
gamma = 0.99
decay_rate = 0.99
render = True  # 是否可视化
# resume = True         # load existing policy network
model_file_name = "model_pong"
np.set_printoptions(threshold=np.nan)def prepro(I):""" prepro 210x160x3 uint8 frame into 6400 (80x80) 1D float vector 将原始输入210×160×3改为80×80=6400的一维向量"""
    I = I[35:195]I = I[::2, ::2, 0]I[I == 144] = 0
    I[I == 109] = 0
    I[I != 0] = 1
    return I.astype(np.float).ravel()env = gym.make("Pong-v0")
observation = env.reset()
prev_x = None
running_reward = None
reward_sum = 0
episode_number = 0
# 用以保存batch_size个Episode的所有状态
xs, ys, rs = [], [], []
# observation for training and inference
t_states = tf.placeholder(tf.float32, shape=[None, D])
# policy network 网络定义
network = InputLayer(t_states, name='input')
network = DenseLayer(network, n_units=H, act=tf.nn.relu, name='hidden')
network = DenseLayer(network, n_units=3, name='output')
probs = network.outputs
sampling_prob = tf.nn.softmax(probs)
# 用折算累计回报与交叉熵的乘积来表示目标函数 由cross_entropy_reward_loss实现
t_actions = tf.placeholder(tf.int32, shape=[None])
t_discount_rewards = tf.placeholder(tf.float32, shape=[None])
loss = tl.rein.cross_entropy_reward_loss(probs, t_actions, t_discount_rewards)
train_op = tf.train.RMSPropOptimizer(learning_rate, decay_rate).minimize(loss)with tf.Session() as sess:tl.layers.initialize_global_variables(sess)# if resume:
    #     load_params = tl.files.load_npz(name=model_file_name+'.npz')
    #     tl.files.assign_params(sess, load_params, network)
    tl.files.load_and_assign_npz(sess, model_file_name + '.npz', network)network.print_params()network.print_layers()start_time = time.time()game_number = 0
    while True:# 是否显示游戏画面
        if render: env.render()# 使用当前帧与前一时间点的帧的差值来定义状态
        cur_x = prepro(observation)x = cur_x - prev_x if prev_x is not None else np.zeros(D)x = x.reshape(1, D)prev_x = cur_x# 策略网络输出各个动作的概率
        prob = sess.run(sampling_prob, feed_dict={t_states: x})# 根据输出的概率来源则动作. 1: STOP  2: UP  3: DOWN
        # action = np.random.choice([1,2,3], p=prob.flatten())
        action = tl.rein.choice_action_by_probs(prob.flatten(), [1, 2, 3])# 执行动作，获取新的状态和回报
        observation, reward, done, _ = env.step(action)# 累加一个Episode的回报，显示使用
        reward_sum += reward# 保存这一步的状态、动作和回报
        xs.append(x)  # all observations in an episode
        ys.append(action - 1)  # all fake labels in an episode (action begins from 1, so minus 1)
        rs.append(reward)  # all rewards in an episode

        # 当一个Episode完结
        if done:episode_number += 1
            game_number = 0
            # 当batch_size个Episode完结，更新一次网络
            if episode_number % batch_size == 0:print('batch over...... updating parameters......')epx = np.vstack(xs)epy = np.asarray(ys)epr = np.asarray(rs)# 求折算累计回报，并把回报归一化，可以使训练更加稳定
                disR = tl.rein.discount_episode_rewards(epr, gamma)disR -= np.mean(disR)disR /= np.std(disR)# 清空列表以下一次保存
                xs, ys, rs = [], [], []# 更新策略网络
                sess.run(train_op, feed_dict={t_states: epx, t_actions: epy, t_discount_rewards: disR})# 保存模型，以供测试使用
            if episode_number % (batch_size * 100) == 0:tl.files.save_npz(network.all_params, name=model_file_name + '.npz')# 每个Episode完结时，显示动态回报
            running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01
            print('resetting env. episode reward total was %f. running mean: %f' % (reward_sum, running_reward))# 重置环境
            reward_sum = 0
            observation = env.reset()  # reset env
            prev_x = None
        # 当一盘乒乓球完成时，显示信息，一个Episode有很多盘球
        if reward != 0:print(('episode %d: game %d took %.5fs, reward: %f' % (episode_number, game_number, \time.time() - start_time, reward)), ('' if reward == -1 else ' !!!!!!!!'))start_time = time.time()game_number += 1