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"""
Reinforcement Learning (A3C) using Pytroch + multiprocessing.
The most simple implementation for continuous action.
View more on my Chinese tutorial page [莫烦Python](https://morvanzhou.github.io/).
"""
import os
import torch.multiprocessing as mp
from .utils import v_wrap, push_and_pull, record
from .shared_adam import SharedAdam
from .net import Net
GAMMA = 0.65
class Worker(mp.Process):
def __init__(self, max_ep, gnet, opt, global_ep, global_ep_r, res_queue, name, env, N_S, N_A, words_list, word_width, winning_ep):
super(Worker, self).__init__()
self.max_ep = max_ep
self.name = 'w%02i' % name
self.g_ep, self.g_ep_r, self.res_queue, self.winning_ep = global_ep, global_ep_r, res_queue, winning_ep
self.gnet, self.opt = gnet, opt
self.word_list = words_list
self.lnet = Net(N_S, N_A, words_list, word_width) # local network
self.env = env.unwrapped
def run(self):
while self.g_ep.value < self.max_ep:
s = self.env.reset()
buffer_s, buffer_a, buffer_r = [], [], []
ep_r = 0.
while True:
a = self.lnet.choose_action(v_wrap(s[None, :]))
s_, r, done, _ = self.env.step(a)
ep_r += r
buffer_a.append(a)
buffer_s.append(s)
buffer_r.append(r)
if done: # update global and assign to local net
# sync
push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA)
goal_word = self.word_list[self.env.goal_word]
record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name, goal_word, self.word_list[a], len(buffer_a), self.winning_ep)
buffer_s, buffer_a, buffer_r = [], [], []
break
s = s_
self.res_queue.put(None)
def train(env, max_ep):
os.environ["OMP_NUM_THREADS"] = "1"
n_s = env.observation_space.shape[0]
n_a = env.action_space.n
words_list = env.words
word_width = len(env.words[0])
gnet = Net(n_s, n_a, words_list, word_width) # global network
gnet.share_memory() # share the global parameters in multiprocessing
opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.92, 0.999)) # global optimizer
global_ep, global_ep_r, res_queue, win_ep = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Value('i', 0)
# parallel training
workers = [Worker(max_ep, gnet, opt, global_ep, global_ep_r, res_queue, i, env, n_s, n_a, words_list, word_width, win_ep) for i in range(mp.cpu_count())]
[w.start() for w in workers]
res = [] # record episode reward to plot
while True:
r = res_queue.get()
if r is not None:
res.append(r)
else:
break
[w.join() for w in workers]
return global_ep, win_ep, gnet, res
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