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train_thread.py
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train_thread.py
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#reference: https://arxiv.org/pdf/1506.02438.pdf
import os
import pickle
import random
import numpy as np
from collections import deque
from mario_wrapper import create_env
from torch.distributions import Categorical
import torch
import torch.nn.functional as F
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT, COMPLEX_MOVEMENT, RIGHT_ONLY
import sys
from a3c_model import a3c
#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
torch.manual_seed(595)
np.random.seed(595)
random.seed(595)
world = 1
stage = 1
actiontype = SIMPLE_MOVEMENT
tau = 1
gamma = 0.9
update = 200
max_episode = int(1e5)
n_step = 50
modelFile = 'a3c_checkpoint3.pth'
scoreFile = 'scores3.data'
def local_train(index,global_model,optimizer,save=False):
torch.manual_seed(100 + index)
done = True
temp_step = 0
env = create_env(world,stage)
local_model = a3c(4,env.action_space.n)
local_model.train()
state = torch.from_numpy(env.reset())
for i_episode in range(max_episode):
if save:
print(i_episode)
local_model.load_state_dict(global_model.state_dict())
if done:
hx = torch.zeros((1,512),dtype=torch.float)
cx = torch.zeros((1,512),dtype=torch.float)
else:
hx = hx.detach()
cx = cx.detach()
log_policies = []
v = []
r = []
entropies = []
for _ in range(n_step):
temp_step+=1
pi,value,hx,cx = local_model(state,hx,cx)
policy = F.softmax(pi,dim=1)
log_policy = F.log_softmax(pi,dim=1)
entropy = -(policy*log_policy).sum(1)
m = Categorical(policy)
action = m.sample().item() #stochastic policy
state, reward, done, _ = env.step(action)
state = torch.from_numpy(state)
log_policies.append(log_policy[0,action])
v.append(value)
r.append(reward)
entropies.append(entropy)
if done:
temp_step=0
state = torch.from_numpy(env.reset())
break
actorL = 0
criticL = 0
R = torch.zeros((1,1),dtype=float)
next_value = R
if not done:
_,R,_,_ = local_model(state,hx,cx) # if the episode is end, we can calculate the R, if not, we use V to estimate the future R
gae = torch.zeros((1,1),dtype=float) # we use generalized advantage estimator (gae) to estimate gradient
for log_policy, value, reward, entropy in list(zip(log_policies,v,r,entropies))[::-1]:
gae = gae*gamma*tau
gae = gae + reward + gamma*next_value.detach() - value.detach()
actorL = actorL - gae*log_policy - 0.01*entropy
next_value = value
R = gamma*R + reward
criticL = criticL + 0.5*((R-value).pow(2))
totalL= actorL + criticL
optimizer.zero_grad()
totalL.backward()
for local_p, global_p in zip(local_model.parameters(),global_model.parameters()):
if global_p.grad is not None:
break
global_p._grad = local_p.grad
optimizer.step()
if save:
if i_episode%update== 0:
torch.save(global_model.state_dict(), modelFile)
def local_test(index,global_model):
scores = []
if os.path.exists(scoreFile):
print('score exist')
with open(scoreFile, 'rb') as sc1:
scores = pickle.load(sc1)
torch.manual_seed(123 + index)
done = True
temp_step = 0
env = create_env(world,stage)
local_model = a3c(4,env.action_space.n)
local_model.eval()
n_step = 50
state = torch.from_numpy(env.reset())
R = 0
rlist = deque(maxlen=30)
while True:
local_model.load_state_dict(global_model.state_dict())
if done:
hx = torch.zeros((1,512),dtype=torch.float)
cx = torch.zeros((1,512),dtype=torch.float)
else:
hx = hx.detach()
cx = cx.detach()
R = 0
for _ in range(n_step):
temp_step+=1
pi,_,hx,cx = local_model(state,hx,cx)
policy = F.softmax(pi)
action = torch.argmax(policy).item()
state, reward, done, _ = env.step(action)
state = torch.from_numpy(state)
R = R+reward
env.render()
if done:
temp_step=0
state = torch.from_numpy(env.reset())
rlist.append(R)
scores.append(np.mean(rlist))
print("Mean score: {:.4f} present score: {:.4f}".format(np.mean(rlist),R))
with open(scoreFile, 'wb') as sc:
pickle.dump(scores, sc)
R = 0
break