distributional rl

RL/Basic-DisRL-Demo/Categorical_DQN.py

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+import tensorflow as tf
+import numpy as np
+import random
+from collections import deque
+from Config import Categorical_DQN_Config
+from utils import conv, dense
+import math
+
+
+class Categorical_DQN():
+    def __init__(self,env,config):
+        self.sess = tf.InteractiveSession()
+        self.config = config
+        self.v_max = self.config.v_max
+        self.v_min = self.config.v_min
+        self.atoms = self.config.atoms
+
+        self.time_step = 0
+        self.epsilon = self.config.INITIAL_EPSILON
+        self.state_shape = env.observation_space.shape
+        self.action_dim = env.action_space.n
+
+        target_state_shape = [1]
+        target_state_shape.extend(self.state_shape)
+
+        self.state_input = tf.placeholder(tf.float32,target_state_shape)
+        self.action_input = tf.placeholder(tf.int32,[1,1])
+
+        self.m_input = tf.placeholder(tf.float32,[self.atoms])
+
+        self.delta_z = (self.v_max - self.v_min) / (self.atoms - 1)
+        self.z = [self.v_min + i * self.delta_z for i in range(self.atoms)]
+
+        self.build_cate_dqn_net()
+
+        self.saver = tf.train.Saver()
+
+        self.sess.run(tf.global_variables_initializer())
+
+        self.save_model()
+        self.restore_model()
+
+
+
+
+    def build_layers(self, state, action, c_names, units_1, units_2, w_i, b_i, reg=None):
+        with tf.variable_scope('conv1'):
+            conv1 = conv(state, [5, 5, 3, 6], [6], [1, 2, 2, 1], w_i, b_i)
+        with tf.variable_scope('conv2'):
+            conv2 = conv(conv1, [3, 3, 6, 12], [12], [1, 2, 2, 1], w_i, b_i)
+        with tf.variable_scope('flatten'):
+            flatten = tf.contrib.layers.flatten(conv2)
+
+        with tf.variable_scope('dense1'):
+            dense1 = dense(flatten, units_1, [units_1], w_i, b_i)
+        with tf.variable_scope('dense2'):
+            dense2 = dense(dense1, units_2, [units_2], w_i, b_i)
+        with tf.variable_scope('concat'):
+            concatenated = tf.concat([dense2, tf.cast(action, tf.float32)], 1)
+        with tf.variable_scope('dense3'):
+            dense3 = dense(concatenated, self.atoms, [self.atoms], w_i, b_i) # 返回
+        return dense3
+
+    def build_cate_dqn_net(self):
+        with tf.variable_scope('target_net'):
+            c_names = ['target_net_arams',tf.GraphKeys.GLOBAL_VARIABLES]
+            w_i = tf.random_uniform_initializer(-0.1,0.1)
+            b_i = tf.constant_initializer(0.1)
+            self.z_target = self.build_layers(self.state_input,self.action_input,c_names,24,24,w_i,b_i)
+
+        with tf.variable_scope('eval_net'):
+            c_names = ['eval_net_params',tf.GraphKeys.GLOBAL_VARIABLES]
+            w_i = tf.random_uniform_initializer(-0.1,0.1)
+            b_i = tf.constant_initializer(0.1)
+            self.z_eval = self.build_layers(self.state_input,self.action_input,c_names,24,24,w_i,b_i)
+
+
+        self.q_eval = tf.reduce_sum(self.z_eval * self.z)
+        self.q_target = tf.reduce_sum(self.z_target * self.z)
+
+        self.cross_entropy_loss = -tf.reduce_sum(self.m_input * tf.log(self.z_eval))
+
+        self.optimizer = tf.train.AdamOptimizer(self.config.LEARNING_RATE).minimize(self.cross_entropy_loss)
+
+
+
+
+    def train(self,s,r,action,s_,gamma):
+        list_q_ = [self.sess.run(self.q_target,feed_dict={self.state_input:[s_],self.action_input:[[a]]}) for a in range(self.action_dim)]
+        a_ = tf.argmax(list_q_).eval()
+        m = np.zeros(self.atoms)
+        p = self.sess.run(self.z_target,feed_dict = {self.state_input:[s_],self.action_input:[[a_]]})[0]
+        for j in range(self.atoms):
+            Tz = min(self.v_max,max(self.v_min,r+gamma * self.z[j]))
+            bj = (Tz - self.v_min) / self.delta_z # 分在第几个块里
+            l,u = math.floor(bj),math.ceil(bj) # 上下界
+
+            pj = p[j]
+
+            m[int(l)] += pj * (u - bj)
+            m[int(u)] += pj * (bj - l)
+
+        self.sess.run(self.optimizer,feed_dict={self.state_input:[s] , self.action_input:[action], self.m_input: m })
+
+
+
+    def save_model(self):
+        print("Model saved in : ", self.saver.save(self.sess, self.config.MODEL_PATH))
+
+    def restore_model(self):
+        self.saver.restore(self.sess, self.config.MODEL_PATH)
+        print("Model restored.")
+
+
+    def greedy_action(self,s):
+        self.epsilon = max(self.config.FINAL_EPSILON, self.epsilon * self.config.EPSILIN_DECAY)
+        if random.random() <= self.epsilon:
+            return random.randint(0, self.action_dim - 1)
+        return np.argmax(
+            [self.sess.run(self.q_target,feed_dict={self.state_input:[s],self.action_input:[[a]]}) for a in range(self.action_dim)])

RL/Basic-DisRL-Demo/Config.py

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+class Categorical_DQN_Config():
+    v_min = 0
+    v_max = 1000
+    atoms = 51
+
+    # ENV_NAME = "CartPole-v1"
+    ENV_NAME = 'Breakout-v0'  # 0: hold   1: throw the ball   2: move right   3: move left
+    # ENV_NAME = "Freeway-v0"
+    GAMMA = 0.99  # discount factor for target Q
+    START_TRAINING = 1000  # experience replay buffer size
+    BATCH_SIZE = 64  # size of minibatch
+    UPDATE_TARGET_NET = 400  # update eval_network params every 200 steps
+    LEARNING_RATE = 0.01
+    MODEL_PATH = './model/C51DQN_model'
+
+    INITIAL_EPSILON = 0.9  # starting value of epsilon
+    FINAL_EPSILON = 0.05  # final value of epsilon
+    EPSILIN_DECAY = 0.9999
+
+    replay_buffer_size = 2000
+    iteration = 5
+    episode = 300  # 300 games per iteration

RL/Basic-DisRL-Demo/main.py

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+
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import gym
+import numpy as np
+import pickle
+from Config import Categorical_DQN_Config
+from Categorical_DQN import Categorical_DQN
+
+
+def map_scores(dqfd_scores=None, ddqn_scores=None, xlabel=None, ylabel=None):
+    if dqfd_scores is not None:
+        plt.plot(dqfd_scores, 'r')
+    if ddqn_scores is not None:
+        plt.plot(ddqn_scores, 'b')
+    if xlabel is not None:
+        plt.xlabel(xlabel)
+    if ylabel is not None:
+        plt.ylabel(ylabel)
+    plt.show()
+
+def BreakOut_CDQN(index, env):
+    with tf.variable_scope('DQfD_' + str(index)):
+        agent = Categorical_DQN(env, Categorical_DQN_Config())
+    scores = []
+    for e in range(Categorical_DQN_Config.episode):
+        done = False
+        score = 0  # sum of reward in one episode
+        state = env.reset()
+        # while done is False:
+        last_lives = 5
+        throw = True
+        items_buffer = []
+        while not done:
+            env.render()
+            action = 1 if throw else agent.greedy_action(state)
+            next_state, real_reward, done, info = env.step(action)
+            lives = info['ale.lives']
+            train_reward = 1 if throw else -1 if lives < last_lives else real_reward
+            score += real_reward
+            throw = lives < last_lives
+            last_lives = lives
+            # agent.train(state, train_reward, [action], next_state, 0.1)
+            items_buffer.append([state, [action], next_state, 0.1])
+            state = next_state
+            if train_reward != 0:  # train when miss the ball or score or throw the ball in the beginning
+                for item in items_buffer:
+                    agent.train(item[0], -1 if throw else train_reward, item[1], item[2], item[3])
+                items_buffer = []
+        scores.append(score)
+        agent.save_model()
+        # if np.mean(scores[-min(10, len(scores)):]) > 495:
+        #     break
+    return scores
+
+
+if __name__ == '__main__':
+    env = gym.make("Breakout-v0")
+    CDQN_sum_scores = np.zeros(Categorical_DQN_Config.episode)
+    for i in range(Categorical_DQN_Config.iteration):
+        scores = BreakOut_CDQN(i,env)
+        c51_sum_scores = [a + b for a, b in zip(scores, CDQN_sum_scores)]
+    C51DQN_mean_scores = CDQN_sum_scores / Categorical_DQN_Config.iteration
+    with open('/Users/mahailong/C51DQN/C51DQN_mean_scores.p', 'wb') as f:
+        pickle.dump(C51DQN_mean_scores, f, protocol=2)

RL/Basic-DisRL-Demo/readme

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+论文：A Distributional Perspective on Reinforcement Learning
+地址：https://arxiv.org/abs/1707.06887

RL/Basic-DisRL-Demo/utils.py

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+import tensorflow as tf
+from tensorflow.python.framework import ops
+
+
+def conv(inputs, kernel_shape, bias_shape, strides, w_i, b_i=None, activation=tf.nn.relu):
+    # 使用tf.layers
+    # relu1 = tf.layers.conv2d(input_imgs, filters=24, kernel_size=[5, 5], strides=[2, 2],
+    #                          padding='SAME', activation=tf.nn.relu,
+    #                          kernel_initializer=w_i, bias_initializer=b_i)
+    weights = tf.get_variable('weights', shape=kernel_shape, initializer=w_i)
+    conv = tf.nn.conv2d(inputs, weights, strides=strides, padding='SAME')
+    if bias_shape is not None:
+        biases = tf.get_variable('biases', shape=bias_shape, initializer=b_i)
+        return activation(conv + biases) if activation is not None else conv + biases
+    return activation(conv) if activation is not None else conv
+
+
+def dense(inputs, units, bias_shape, w_i, b_i=None, activation=tf.nn.relu):
+    # 使用tf.layers，注意：先flatten
+    # dense1 = tf.layers.dense(tf.contrib.layers.flatten(relu5), activation=tf.nn.relu, units=50)
+    if not isinstance(inputs, ops.Tensor):
+        inputs = ops.convert_to_tensor(inputs, dtype='float')
+        # dim_list = inputs.get_shape().as_list()
+        # flatten_shape = dim_list[1] if len(dim_list) <= 2 else reduce(lambda x, y: x * y, dim_list[1:])
+        # reshaped = tf.reshape(inputs, [dim_list[0], flatten_shape])
+    if len(inputs.shape) > 2:
+        inputs = tf.contrib.layers.flatten(inputs)
+    flatten_shape = inputs.shape[1]
+    weights = tf.get_variable('weights', shape=[flatten_shape, units], initializer=w_i)
+    dense = tf.matmul(inputs, weights)
+    if bias_shape is not None:
+        assert bias_shape[0] == units
+        biases = tf.get_variable('biases', shape=bias_shape, initializer=b_i)
+        return activation(dense + biases) if activation is not None else dense + biases
+    return activation(dense) if activation is not None else dense

basic/.ipynb_checkpoints/class_and_instance-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}