diff --git a/policy gradient/pytroch_MountainCar-v0.py b/policy gradient/pytroch_MountainCar-v0.py
new file mode 100644
index 0000000..e1e83ec
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+++ b/policy gradient/pytroch_MountainCar-v0.py	
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+# MountainCar V0
+
+import numpy as np
+import gym
+import matplotlib.pyplot as plt
+from itertools import count
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.optim import adam
+from torch.distributions import Categorical
+
+env = gym.make('MountainCar-v0')
+env = env.unwrapped
+env.seed(1)
+
+torch.manual_seed(1)
+plt.ion()
+
+
+#Hyperparameters
+learning_rate = 0.02
+gamma = 0.995
+episodes = 1000
+
+eps = np.finfo(np.float32).eps.item()
+
+action_space = env.action_space.n
+state_space = env.observation_space.shape[0]
+
+
+class Policy(nn.Module):
+    def __init__(self):
+        super(Policy, self).__init__()
+
+        self.fc1 = nn.Linear(state_space, 20)
+        #self.fc2 = nn.Linear(128,64)
+        self.fc3 = nn.Linear(20, action_space)
+
+        self.gamma = gamma
+        self.saved_log_probs = []
+        self.rewards = []
+
+    def forward(self, x):
+
+        x = F.relu(self.fc1(x))
+        #x = F.relu(self.fc2(x))
+        x = F.softmax(self.fc3(x), dim=1)
+
+        return x
+
+policy = Policy()
+optimizer = adam.Adam(policy.parameters(), lr=learning_rate)
+
+def selct_action(state):
+    state = torch.from_numpy(state).float().unsqueeze(0)
+    probs = policy(state)
+    c = Categorical(probs)
+    action = c.sample()
+
+
+    policy.saved_log_probs.append(c.log_prob(action))
+    action = action.item()
+    return action
+
+def finish_episode():
+    R = 0
+    policy_loss = []
+    rewards = []
+
+    for r in policy.rewards[::-1]:
+        R = r + policy.gamma * R
+        rewards.insert(0, R)
+
+    # Formalize reward
+    rewards = torch.tensor(rewards)
+    rewards = (rewards - rewards.mean())/(rewards.std() + eps)
+
+    # get loss
+    for reward, log_prob in zip(rewards, policy.saved_log_probs):
+        policy_loss.append(-log_prob * reward)
+
+    optimizer.zero_grad()
+    policy_loss = torch.cat(policy_loss).sum()
+    policy_loss.backward()
+    optimizer.step()
+
+
+
+    del policy.rewards[:]
+    del policy.saved_log_probs[:]
+
+def plot(steps):
+    ax = plt.subplot(111)
+    ax.cla()
+    ax.set_title('Training')
+    ax.set_xlabel('Episode')
+    ax.set_ylabel('Run Time')
+    ax.plot(steps)
+    RunTime = len(steps)
+    path =  './PG_MountainCar-v0/'+'RunTime'+str(RunTime)+'.jpg'
+    if len(steps) % 100 == 0:
+        plt.savefig(path)
+    plt.pause(0.0000001)
+
+
+
+def main():
+
+    running_reward = 0
+    steps = []
+    for episode in count(60000):
+        state = env.reset()
+
+        for t in range(10000):
+            action = selct_action(state)
+            state, reward ,done, info = env.step(action)
+            env.render()
+            policy.rewards.append(reward)
+
+            if done:
+                print("Episode {}, live time = {}".format(episode, t))
+                steps.append(t)
+                plot(steps)
+                break
+        if episode % 50 == 0:
+            torch.save(policy, 'policyNet.pkl')
+
+        running_reward = running_reward * policy.gamma - t*0.01
+        finish_episode()
+
+if __name__ == '__main__':
+    main()