update projects

projects/codes/TD3/memory.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-04-13 11:00:13
+LastEditor: John
+LastEditTime: 2021-04-15 01:25:14
+Discription: 
+Environment: 
+'''
+import numpy as np
+import torch
+
+
+class ReplayBuffer(object):
+	def __init__(self, n_states, n_actions, max_size=int(1e6)):
+		self.max_size = max_size
+		self.ptr = 0
+		self.size = 0
+		self.state = np.zeros((max_size, n_states))
+		self.action = np.zeros((max_size, n_actions))
+		self.next_state = np.zeros((max_size, n_states))
+		self.reward = np.zeros((max_size, 1))
+		self.not_done = np.zeros((max_size, 1))
+		self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+	def push(self, state, action, next_state, reward, done):
+		self.state[self.ptr] = state
+		self.action[self.ptr] = action
+		self.next_state[self.ptr] = next_state
+		self.reward[self.ptr] = reward
+		self.not_done[self.ptr] = 1. - done
+		self.ptr = (self.ptr + 1) % self.max_size
+		self.size = min(self.size + 1, self.max_size)
+
+	def sample(self, batch_size):
+		ind = np.random.randint(0, self.size, size=batch_size)
+		return (
+			torch.FloatTensor(self.state[ind]).to(self.device),
+			torch.FloatTensor(self.action[ind]).to(self.device),
+			torch.FloatTensor(self.next_state[ind]).to(self.device),
+			torch.FloatTensor(self.reward[ind]).to(self.device),
+			torch.FloatTensor(self.not_done[ind]).to(self.device)
+		)