mirror of
https://github.com/wassname/DeepRL.git
synced 2026-10-28 12:30:38 +08:00
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
#######################################################################
|
|
# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
|
|
# Permission given to modify the code as long as you keep this #
|
|
# declaration at the top #
|
|
#######################################################################
|
|
|
|
import numpy as np
|
|
|
|
class Replay:
|
|
def __init__(self, memory_size, batch_size):
|
|
self.memory_size = memory_size
|
|
self.batch_size = batch_size
|
|
|
|
self.states = []
|
|
self.actions = []
|
|
self.rewards = []
|
|
self.next_states = []
|
|
self.terminals = []
|
|
|
|
|
|
def feed(self, experience):
|
|
state, action, reward, next_state, done = experience
|
|
self.states.append(state)
|
|
self.actions.append(action)
|
|
self.rewards.append(reward)
|
|
self.next_states.append(next_state)
|
|
self.terminals.append(done)
|
|
if len(self.terminals) > self.memory_size:
|
|
self.states.pop(0)
|
|
self.actions.pop(0)
|
|
self.rewards.pop(0)
|
|
self.next_states.pop(0)
|
|
self.terminals.pop(0)
|
|
|
|
def sample(self):
|
|
if len(self.terminals) >= self.batch_size:
|
|
sampled_indices = np.arange(len(self.terminals))
|
|
np.random.shuffle(sampled_indices)
|
|
sampled_indices = sampled_indices[: self.batch_size]
|
|
return [np.asarray(self.states)[sampled_indices],
|
|
np.asarray(self.actions)[sampled_indices],
|
|
np.asarray(self.rewards)[sampled_indices],
|
|
np.asarray(self.next_states)[sampled_indices],
|
|
np.asarray(self.terminals)[sampled_indices]]
|
|
return None
|
|
|