mirror of
https://github.com/wassname/Deep-reinforcement-learning-with-pytorch.git
synced 2026-09-25 13:10:09 +08:00
200 lines
8.0 KiB
Python
200 lines
8.0 KiB
Python
import numpy as np
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import pandas as pd
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import tensorflow as tf
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np.random.seed(1)
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tf.set_random_seed(1)
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# Deep Q Network off-policy
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class DeepQNetwork:
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def __init__(
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self,
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n_actions,
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n_features,
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learning_rate=0.01,
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reward_decay=0.9,
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e_greedy=0.9,
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replace_target_iter=300,
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memory_size=500,
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batch_size=32,
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e_greedy_increment=None,
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output_graph=False,
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):
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self.n_actions = n_actions
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self.n_features = n_features
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self.lr = learning_rate
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self.gamma = reward_decay
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self.epsilon_max = e_greedy
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self.replace_target_iter = replace_target_iter
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self.memory_size = memory_size
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self.batch_size = batch_size
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self.epsilon_increment = e_greedy_increment
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self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max
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# total learning step
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self.learn_step_counter = 0
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# initialize zero memory [s, a, r, s_]
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self.memory = np.zeros((self.memory_size, n_features * 2 + 2))
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# consist of [target_net, evaluate_net]
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self._build_net()
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t_params = tf.get_collection('target_net_params')
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e_params = tf.get_collection('eval_net_params')
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self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
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self.sess = tf.Session()
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if output_graph:
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# $ tensorboard --logdir=logs
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# tf.train.SummaryWriter soon be deprecated, use following
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tf.summary.FileWriter("logs/", self.sess.graph)
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self.sess.run(tf.global_variables_initializer())
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self.cost_his = []
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def _build_net(self):
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# ------------------ build evaluate_net ------------------
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self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s') # input
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self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target') # for calculating loss
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with tf.variable_scope('eval_net'):
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# c_names(collections_names) are the collections to store variables
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c_names, n_l1, w_initializer, b_initializer = \
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['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 10, \
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tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers
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# first layer. collections is used later when assign to target net
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with tf.variable_scope('l1'):
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w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
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b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)
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l1 = tf.nn.relu(tf.matmul(self.s, w1) + b1)
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# second layer. collections is used later when assign to target net
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with tf.variable_scope('l2'):
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w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
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b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
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self.q_eval = tf.matmul(l1, w2) + b2
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with tf.variable_scope('loss'):
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self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))
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with tf.variable_scope('train'):
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self._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)
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# ------------------ build target_net ------------------
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self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input
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with tf.variable_scope('target_net'):
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# c_names(collections_names) are the collections to store variables
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c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]
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# first layer. collections is used later when assign to target net
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with tf.variable_scope('l1'):
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w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
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b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)
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l1 = tf.nn.relu(tf.matmul(self.s_, w1) + b1)
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# second layer. collections is used later when assign to target net
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with tf.variable_scope('l2'):
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w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
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b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
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self.q_next = tf.matmul(l1, w2) + b2
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def store_transition(self, s, a, r, s_):
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if not hasattr(self, 'memory_counter'):
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self.memory_counter = 0
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transition = np.hstack((s, [a, r], s_))
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# replace the old memory with new memory
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index = self.memory_counter % self.memory_size
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self.memory[index, :] = transition
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self.memory_counter += 1
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def choose_action(self, observation):
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# to have batch dimension when feed into tf placeholder
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observation = observation[np.newaxis, :]
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if np.random.uniform() < self.epsilon:
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# forward feed the observation and get q value for every actions
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actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})
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action = np.argmax(actions_value)
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else:
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action = np.random.randint(0, self.n_actions)
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return action
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def learn(self):
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# check to replace target parameters
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if self.learn_step_counter % self.replace_target_iter == 0:
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self.sess.run(self.replace_target_op)
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print('\ntarget_params_replaced\n')
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# sample batch memory from all memory
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if self.memory_counter > self.memory_size:
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sample_index = np.random.choice(self.memory_size, size=self.batch_size)
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else:
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sample_index = np.random.choice(self.memory_counter, size=self.batch_size)
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batch_memory = self.memory[sample_index, :]
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q_next, q_eval = self.sess.run(
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[self.q_next, self.q_eval],
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feed_dict={
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self.s_: batch_memory[:, -self.n_features:], # fixed params
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self.s: batch_memory[:, :self.n_features], # newest params
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})
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# change q_target w.r.t q_eval's action
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q_target = q_eval.copy()
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batch_index = np.arange(self.batch_size, dtype=np.int32)
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eval_act_index = batch_memory[:, self.n_features].astype(int)
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reward = batch_memory[:, self.n_features + 1]
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q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)
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"""
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For example in this batch I have 2 samples and 3 actions:
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q_eval =
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[[1, 2, 3],
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[4, 5, 6]]
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q_target = q_eval =
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[[1, 2, 3],
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[4, 5, 6]]
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Then change q_target with the real q_target value w.r.t the q_eval's action.
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For example in:
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sample 0, I took action 0, and the max q_target value is -1;
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sample 1, I took action 2, and the max q_target value is -2:
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q_target =
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[[-1, 2, 3],
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[4, 5, -2]]
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So the (q_target - q_eval) becomes:
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[[(-1)-(1), 0, 0],
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[0, 0, (-2)-(6)]]
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We then backpropagate this error w.r.t the corresponding action to network,
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leave other action as error=0 cause we didn't choose it.
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"""
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# train eval network
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_, self.cost = self.sess.run([self._train_op, self.loss],
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feed_dict={self.s: batch_memory[:, :self.n_features],
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self.q_target: q_target})
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self.cost_his.append(self.cost)
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# increasing epsilon
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self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max
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self.learn_step_counter += 1
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def plot_cost(self):
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import matplotlib.pyplot as plt
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plt.plot(np.arange(len(self.cost_his)), self.cost_his)
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plt.ylabel('Cost')
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plt.xlabel('training steps')
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plt.show()
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