frameworks

dqn.py

@@ -1,6 +1,15 @@
+from collections import deque
+
 import numpy as np
 import torch
 
+import GEO_parameters
+from Replay_memory import Memory, Transaction
+from model import Net
+import torch.nn.functional as f
+import matplotlib.pyplot as plt
+from GEO_parameters import GEO_Parameters
+
 
 def dev0(a, b):
     a = a.detach().numpy()
@@ -23,3 +32,149 @@ def hard_update(target, source):
 
 
 class DeepQNetwork:
+    def __init__(self, cap, n_actions, n_states, learning_rate, reward_decay, e_greedy, replace_target_iter, batch_size,e_greedy_increment=None):
+
+        self.memory = Memory(cap)
+        self.batch = None
+        self.state_batch = batch_size
+        self.action_batch = None
+        self.reward_batch = None
+        self.non_final_next_states = None
+        self.expected_state_action_values = None
+        self.state_action_values = None
+
+        self.n_states = n_states
+        self.n_actions = n_actions
+        self.lr = learning_rate
+        self.gamma = reward_decay
+        self.epsilon_max = e_greedy
+        self.replace_target_iter = replace_target_iter
+        self.batch_size = batch_size
+        self.epsilon_decay = e_greedy_increment
+        self.epsilon = 0 if e_greedy_increment is None else self.epsilon_max
+
+
+        self.learn_step_counter = 1
+
+        self.counter = GEO_parameters.state_dim
+        self.main_q_network = Net(n_states, n_actions) # 输入的nstates 应该是M_c+1
+        self.target_q_network = Net(n_states, n_actions)
+
+        self.optimizer = torch.optim.Adam(self.main_q_network.parameters(), self.lr)
+
+        self.cost_his = []
+
+        self.weight_dir = 'weight_DQN'
+
+    def replay(self):
+        if self.learn_step_counter % self.replace_target_iter == 0:
+            self.update_target_q_network()
+
+        if len(self.memory) <self.batch_size:
+            return
+
+        self.batch, self.state_batch, self.action_batch, self.reward_batch, self.non_final_next_states = self.make_mini_batch()
+        self.expected_state_action_values = self.get_expected_state_action_values()
+        self.update_main_q_network()
+        self.epsilon *= 0.9998
+        self.learn_step_counter += 1
+
+    def make_mini_batch(self):
+        transitions = self.memory.sample(self.batch_size)
+
+        batch = Transaction(*zip(*transitions))
+        state_batch = torch.cat(batch.state)
+        action_batch = torch.cat(batch.action)
+        reward_batch = torch.cat(batch.reward)
+        non_final_next_states = torch.cat([s for s in batch.next_state if s is not None])
+        return batch, state_batch, action_batch, reward_batch, non_final_next_states
+
+    def get_expected_state_action_values(self):
+        self.main_q_network.eval()
+        self.target_q_network.eval()
+
+        self.state_action_values = self.main_q_network
+
+        non_final_mask = torch.ByteTensor(tuple(map(lambda x : x is not None, self.batch.next_state))).bool()
+
+        next_state_values = torch.zeros(self.batch_size)
+
+        a_m = torch.zeros(self.batch_size).type(torch.LongTensor)
+
+        a_m[non_final_mask] = self.main_q_network(self.non_final_next_states).detach().max(1)[1]
+
+        a_m_non_final_next_states = a_m[non_final_mask].view(-1, 1)
+
+        next_state_values[non_final_mask] = self.target_q_network(
+            self.non_final_next_states
+        ).gather(1, a_m_non_final_next_states).detach().squeeze()
+
+        expected_state_action_values = self.reward_batch + self.gamma * next_state_values.view(-1, 1)
+        return expected_state_action_values
+
+    def decide_action(self, state):
+        # self.epsilon = 0.5 * (1 / (self.epsilon + 1))
+        if self.epsilon < np.random.uniform():
+            self.main_q_network.eval()
+            with torch.no_grad():
+                action = self.main_q_network(state).detach().max(1)[1]
+        else:
+            action = self.LongTenser([np.random.randint(0, self.n_actions)])
+        return action.view(1, 1)
+
+    def update_main_q_network(self):
+        self.main_q_network.train()
+
+        loss = f.mse_loss(self.state_action_values, self.expected_state_action_values, reduction = 'mean')
+        self.cost_his.append(loss.item())
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+    def update_target_q_network(self):
+        self.target_q_network.load_state_dict(self.main_q_network.state_dict())
+
+    def plot_cost(self):
+        plt.plot(np.arange(len(self.cost_his)), self.cost_his)
+        plt.xlabel('training step')
+        plt.ylabel('cost')
+        plt.show()
+
+    def binary(self, q):
+        self.counter = GEO_Parameters.state_dim - 1
+        t = torch.unsqueeze(q.max(1)[1], 1)
+        temp = torch.zeros_like(t)
+        temp_extend = temp.repeat(1, GEO_Parameters.state_dim)
+        n = temp
+        while torch.logical_not(torch.eq(n, t)):
+            t1 = t % 2
+            t2 = t >> 1
+            numColumns = temp_extend.shape[1]
+            new_tensor_left = temp_extend[:, :self.counter]
+            new_tensor_right = temp_extend[:, self.counter+1:numColumns]
+            temp_extend = torch.cat([new_tensor_left, new_tensor_right], 1)
+            self.counter -= 1
+
+        a1 = temp_extend
+        a1 = a1.float()
+        return a1
+
+    def load_weight_from_pkl(self, file_name):
+        checkpoint = torch.load(file_name)
+        self.main_q_network.load_state_dict(checkpoint['main_q_network_state_dict'])
+        self.target_q_network.load_state_dict(checkpoint['target_q_network_state_dict'])
+        self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+        self.cost_his = checkpoint['loss']
+
+    def save_weight_to_pkl(self, file_name):
+        torch.save({
+            # 'epoch': epochs,
+            'main_q_network_state_dict': self.main_q_network.state_dict(),
+            'target_q_network_state_dict': self.target_q_network.state_dict(),
+            'optimizer_state_dict': self.optimizer.state_dict(),
+            'loss': self.cost_his,
+        }, file_name)
+
+    def update_replay_buffer(self, state, action, reward, next_state):
+        self.memory.push(state, action, reward, next_state)
+