Create DQN_mountain_car.py

dqn/DQN_mountain_car.py

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+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import optim
+import matplotlib.pyplot as plt
+import gym
+
+#hyper parameters
+
+EPSILON = 0.9
+GAMMA = 0.9
+LR = 0.01
+MEMORY_CAPACITY = 200
+Q_NETWORK_ITERATION = 50
+BATCH_SIZE = 4
+
+
+env = gym.make('MountainCar-v0')
+
+NUM_STATES = env.state_space.n
+NUM_ACTIONS = 2
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.num_states = NUM_STATES
+        self.num_actions = NUM_ACTIONS
+        self.fc1 = nn.Linear(self.num_states, 10)
+        self.fc1.weight.data.normal_(0, 0.1)
+        self.fc2 = nn.Linear(10, self.num_actions)
+        self.fc2.weight.data.normal_(0, 0.1)
+
+    def forward(self, state):
+        state = self.fc1(state)
+        state = F.relu(state)
+        action = self.fc2(state)
+
+        return action
+
+class Dqn():
+    def __init__(self):
+        self.eval_net, self.target_net = Net(), Net()
+        self.memory = np.zeros((MEMORY_CAPACITY, 4))
+        self.memory_counter = 0
+        self.learn_counter = 0
+        # state, action ,reward and next state 4
+
+    def store_trans(self, state, action, reward, next_state):
+        index = self.memory_counter % MEMORY_CAPACITY
+        trans = np.hstack((state, action, reward, next_state))
+        self.memory[index,] = trans
+        self.memory_counter += 1
+
+    def choose_action(self, state):
+        # EPSILON
+        if np.random.randn() <= EPSILON:
+            action_value = self.eval_net.forward(state)
+            action = torch.max(action_value, 1)[0].data.numpy()
+        else:
+            action = np.random.choice()
+