add some codes

codes/dqn_cnn/dqn.py

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+import random
+import math
+import torch
+import torch.optim as optim
+import torch.nn.functional as F
+from memory import ReplayBuffer
+from model import CNN
+
+
+class DQN:
+    def __init__(self, screen_height=0, screen_width=0, n_actions=0, gamma=0.999, epsilon_start=0.9, epsilon_end=0.05, epsilon_decay=200, memory_capacity=10000, batch_size=128, device="cpu"):
+        self.actions_count = 0
+        self.n_actions = n_actions
+        self.device = device
+        self.gamma = gamma
+        self.epsilon = 0
+        self.epsilon_start = epsilon_start
+        self.epsilon_end = epsilon_end
+        self.epsilon_decay = epsilon_decay
+        self.batch_size = batch_size
+        self.policy_net = CNN(screen_height, screen_width,
+                              n_actions).to(self.device)
+        self.target_net = CNN(screen_height, screen_width,
+                              n_actions).to(self.device)
+        self.target_net.load_state_dict(self.policy_net.state_dict())
+        self.target_net.eval()  # 不启用 BatchNormalization 和 Dropout
+        self.optimizer = optim.RMSprop(self.policy_net.parameters())
+        self.loss = 0
+        self.memory = ReplayBuffer(memory_capacity)
+        
+
+    def select_action(self, state):
+        '''choose_action [summary]
+        Args:
+            state [torch tensor]: [description]
+        Returns:
+            actions [torch tensor]: [description]
+        '''
+        sample = random.random()
+        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
+            math.exp(-1. * self.actions_count / self.epsilon_decay)
+        self.actions_count += 1
+        if sample > self.epsilon:
+            with torch.no_grad():
+                # t.max(1) will return largest column value of each row.
+                # second column on max result is index of where max element was
+                # found, so we pick action with the larger expected reward.
+                
+                q_value = self.policy_net(state) # q_value比如tensor([[-0.2522,  0.3887]])
+                action = q_value.max(1)[1].view(1, 1)  # q_value最大对应的下标，注意该action是个张量，如tensor([1])
+                return action
+        else:
+            return torch.tensor([[random.randrange(self.n_actions)]], device=self.device, dtype=torch.long)
+
+    def update(self):
+        if len(self.memory) < self.batch_size:
+            return
+        transitions = self.memory.sample(self.batch_size)
+        # Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for
+        # detailed explanation). This converts batch-array of Transitions
+        # to Transition of batch-arrays.
+        batch = self.memory.Transition(*zip(*transitions))
+
+        # Compute a mask of non-final states and concatenate the batch elements
+        # (a final state would've been the one after which simulation ended)
+        non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
+                                                batch.next_state)), device=self.device, dtype=torch.bool)
+        
+        non_final_next_states = torch.cat([s for s in batch.next_state
+                                           if s is not None])
+        state_batch = torch.cat(batch.state)   
+        action_batch = torch.cat(batch.action) 
+        reward_batch = torch.cat(batch.reward) # tensor([1., 1.,...,])
+        
+
+        # Compute Q(s_t, a) - the model computes Q(s_t), then we select the
+        # columns of actions taken. These are the actions which would've been taken
+        # for each batch state according to policy_net
+        state_action_values = self.policy_net(
+            state_batch).gather(1, action_batch) #tensor([[ 1.1217],...,[ 0.8314]])
+
+        # Compute V(s_{t+1}) for all next states.
+        # Expected values of actions for non_final_next_states are computed based
+        # on the "older" target_net; selecting their best reward with max(1)[0].
+        # This is merged based on the mask, such that we'll have either the expected
+        # state value or 0 in case the state was final.
+        next_state_values = torch.zeros(self.batch_size, device=self.device)
+
+        next_state_values[non_final_mask] = self.target_net(
+            non_final_next_states).max(1)[0].detach()
+
+        # Compute the expected Q values
+        expected_state_action_values = (next_state_values * self.gamma) + reward_batch # tensor([0.9685, 0.9683,...,])
+        
+        # Compute Huber loss
+        self.loss = F.smooth_l1_loss(
+            state_action_values, expected_state_action_values.unsqueeze(1))  # .unsqueeze增加一个维度
+        # Optimize the model
+        self.optimizer.zero_grad() # zero_grad clears old gradients from the last step (otherwise you’d just accumulate the gradients from all loss.backward() calls).
+        self.loss.backward() # loss.backward() computes the derivative of the loss w.r.t. the parameters (or anything requiring gradients) using backpropagation.
+        for param in self.policy_net.parameters(): # clip防止梯度爆炸
+            param.grad.data.clamp_(-1, 1)
+        self.optimizer.step() # causes the optimizer to take a step based on the gradients of the parameters.
+
+
+if __name__ == "__main__":
+    dqn = DQN()