#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2021-03-23 15:30:46
LastEditor: John
LastEditTime: 2021-03-23 15:30:55
Discription: 
Environment: 
'''
import numpy as np
class PPOMemory:
    def __init__(self, batch_size):
        self.states = []
        self.probs = []
        self.vals = []
        self.actions = []
        self.rewards = []
        self.dones = []
        self.batch_size = batch_size
    def sample(self):
        batch_step = np.arange(0, len(self.states), self.batch_size)
        indices = np.arange(len(self.states), dtype=np.int64)
        np.random.shuffle(indices)
        batches = [indices[i:i+self.batch_size] for i in batch_step]
        return np.array(self.states),\
                np.array(self.actions),\
                np.array(self.probs),\
                np.array(self.vals),\
                np.array(self.rewards),\
                np.array(self.dones),\
                batches

    def push(self, state, action, probs, vals, reward, done):
        self.states.append(state)
        self.actions.append(action)
        self.probs.append(probs)
        self.vals.append(vals)
        self.rewards.append(reward)
        self.dones.append(done)

    def clear(self):
        self.states = []
        self.probs = []
        self.actions = []
        self.rewards = []
        self.dones = []
        self.vals = []