update

codes/TD3/main.py

@@ -0,0 +1,14 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 23:38:13
+@LastEditor: John
+@LastEditTime: 2020-06-11 23:38:31
+@Discription: 
+@Environment: python 3.7.7
+'''
+import torch
+if __name__ == "__main__":
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

codes/TD3/memory.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-10 15:27:16
+@LastEditor: John
+@LastEditTime: 2020-06-11 21:04:50
+@Discription: 
+@Environment: python 3.7.7
+'''
+import random
+import numpy as np
+
+class ReplayBuffer:
+    
+    def __init__(self, capacity):
+        self.capacity = capacity
+        self.buffer = []
+        self.position = 0
+    
+    def push(self, state, action, reward, next_state, done):
+        if len(self.buffer) < self.capacity:
+            self.buffer.append(None)
+        self.buffer[self.position] = (state, action, reward, next_state, done)
+        self.position = (self.position + 1) % self.capacity
+    
+    def sample(self, batch_size):
+        batch = random.sample(self.buffer, batch_size)
+        state, action, reward, next_state, done = map(np.stack, zip(*batch))
+        return state, action, reward, next_state, done
+    
+    def __len__(self):
+        return len(self.buffer)