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SAC_apex.py
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SAC_apex.py
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import argparse
import json
import os
import gym
import time
import torch
import copy
import numpy as np
import torch.nn as nn
import torch.multiprocessing as mp
from torch.optim import Adam
from copy import deepcopy
from torch.utils.tensorboard import SummaryWriter
from OppModeling.atari_wrappers import make_ftg_ram, make_ftg_ram_nonstation
from OppModeling.utils import Counter
from OppModeling.SAC import MLPActorCritic
from OppModeling.CPC import CPC
from OppModeling.ReplayBuffer import ReplayBufferOppo
from games import Soccer, SoccerPLUS
from OppModeling.train_apex import sac
from OppModeling.evaluation_apex import test_func
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# running setting
parser.add_argument('--cuda', default=False, action='store_true')
parser.add_argument('--seed', '-s', type=int, default=0)
parser.add_argument('--n_process', type=int, default=4)
# basic env setting
parser.add_argument('--env', type=str, default="FightingiceDataFrameskip-v0")
parser.add_argument('--p2', type=str, default="Toothless")
# non station agent settings
parser.add_argument('--non_station', default=False, action='store_true')
parser.add_argument('--stable', default=False, action='store_true')
parser.add_argument('--opp_freq', type=int, default=1)
parser.add_argument('--opp_list', nargs='+')
parser.add_argument('--opp_num', type=int, default=2)
parser.add_argument('--opp1_per', type=float, default=0.5)
parser.add_argument('--opp3_per', type=float, default=0.5)
parser.add_argument('--p', type=float, default=0.5)
# sac setting
parser.add_argument('--replay_size', type=int, default=20000)
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--hid', type=int, default=256)
parser.add_argument('--l', type=int, default=2, help="layers")
parser.add_argument('--episode', type=int, default=20000)
parser.add_argument('--update_after', type=int, default=1000)
parser.add_argument('--update_every', type=int, default=1)
parser.add_argument('--max_ep_len', type=int, default=1000)
parser.add_argument('--min_alpha', type=float, default=0.05)
parser.add_argument('--dynamic_alpha', default=False, action="store_true")
parser.add_argument('--gamma', type=float, default=0.95)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--polyak', type=float, default=0.995)
# CPC setting
parser.add_argument('--cpc', default=False, action="store_true")
parser.add_argument('--cpc_batch', type=int, default=256)
parser.add_argument('--z_dim', type=int, default=32)
parser.add_argument('--c_dim', type=int, default=16)
parser.add_argument('--timestep', type=int, default=10)
parser.add_argument('--cpc_update_freq', type=int, default=1, )
parser.add_argument('--forget_percent', type=float, default=0.2, )
# evaluation settings
parser.add_argument('--test_episode', type=int, default=100)
parser.add_argument('--test_every', type=int, default=100)
# only for percentage experiment
# Saving settings
parser.add_argument('--save_freq', type=int, default=500)
parser.add_argument('--exp_name', type=str, default='percent99-1')
parser.add_argument('--save-dir', type=str, default="./exp_po45")
parser.add_argument('--traj_dir', type=str, default="./experiments_per")
parser.add_argument('--model_para', type=str, default="sac.torch")
parser.add_argument('--cpc_para', type=str, default="cpc.torch")
parser.add_argument('--numpy_para', type=str, default="model.numpy")
parser.add_argument('--train_indicator', type=str, default="test.data")
args = parser.parse_args()
# Basic Settings
mp.set_start_method("forkserver")
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
torch.set_num_threads(torch.get_num_threads())
experiment_dir = os.path.join(args.save_dir, args.exp_name)
if not os.path.exists(experiment_dir):
os.makedirs(experiment_dir)
tensorboard_dir = os.path.join(experiment_dir, "runs")
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
main_dir = os.path.join(tensorboard_dir, "train")
if not os.path.exists(main_dir):
os.makedirs(main_dir)
writer = SummaryWriter(log_dir=main_dir)
with open(os.path.join(experiment_dir, "arguments"), 'w') as f:
json.dump(args.__dict__, f, indent=2)
device = torch.device("cuda") if args.cuda else torch.device("cpu")
# env and model setup
ac_kwargs = dict(hidden_sizes=[args.hid] * args.l)
# if args.exp_name == "test":
# env = gym.make("CartPole-v0")
# elif args.non_station:
# env = make_ftg_ram_nonstation(args.env, p2_list=args.opp_list, total_episode=args.opp_freq,stable=args.stable)
# else:
# env = make_ftg_ram(args.env, p2=args.p2)
env = SoccerPLUS()
obs_dim = env.n_features
act_dim = env.n_actions
# create model
global_ac = MLPActorCritic(obs_dim, act_dim, **ac_kwargs)
if args.cpc:
global_cpc = CPC(timestep=args.timestep, obs_dim=obs_dim, hidden_sizes=[args.hid] * args.l, z_dim=args.z_dim,
c_dim=args.c_dim, device=device)
else:
global_cpc = None
# create shared model for actor
global_ac_targ = deepcopy(global_ac)
shared_ac = deepcopy(global_ac).cpu()
# create optimizer
pi_optimizer = Adam(global_ac.pi.parameters(), lr=args.lr, eps=1e-4)
q1_optimizer = Adam(global_ac.q1.parameters(), lr=args.lr, eps=1e-4)
q2_optimizer = Adam(global_ac.q2.parameters(), lr=args.lr, eps=1e-4)
alpha_optim = Adam([global_ac.log_alpha], lr=args.lr, eps=1e-4)
if args.cpc:
cpc_optimizer = Adam(global_cpc.parameters(), lr=args.lr, eps=1e-4)
env.close()
del env
# training setup
T = Counter() # training steps
E = Counter() # training episode
replay_buffer = ReplayBufferOppo(obs_dim=obs_dim, max_size=args.replay_size, cpc=args.cpc,
cpc_model=global_cpc, writer=writer,E=E)
# bufferopp1 = ReplayBufferOppo(obs_dim=obs_dim, max_size=args.replay_size, cpc=args.cpc,
# cpc_model=global_cpc, writer=writer, E=E)
# bufferopp3 = ReplayBufferOppo(obs_dim=obs_dim, max_size=args.replay_size, cpc=args.cpc,
# cpc_model=global_cpc, writer=writer, E=E)
if os.path.exists(os.path.join(args.save_dir, args.exp_name, args.model_para)):
global_ac.load_state_dict(torch.load(os.path.join(args.save_dir, args.exp_name, args.model_para)))
print("load sac model")
if args.cpc:
global_cpc.load_state_dict(torch.load(os.path.join(args.save_dir, args.exp_name, args.cpc_para)))
print("load cpc model")
if os.path.exists(os.path.join(args.save_dir, args.exp_name, args.train_indicator)):
(e, t) = torch.load(os.path.join(args.save_dir, args.exp_name, args.train_indicator))
T.set(t)
E.set(e)
print("load training indicator")
last_updated = 0
last_deliver = 0
last_saved = 0
test_t = 0
if args.cuda:
global_ac.to(device)
global_ac_targ.to(device)
if args.cpc:
global_cpc.to(device)
for p in global_ac_targ.parameters():
p.requires_grad = False
buffer_q = mp.SimpleQueue()
model_q = [mp.SimpleQueue() for _ in range(args.n_process + args.opp_num)] # 1 test model queue
evaluation_queue = list()
processes = []
# Process 0 for evaluation
for rank in range(args.n_process + args.opp_num): # + n opp test process
# Test during training
if rank < args.opp_num:
p = mp.Process(target=test_func, args=(rank, E, T, args, model_q[rank], torch.device("cpu"), tensorboard_dir))
else:
model_q[rank].put(shared_ac.state_dict())
p = mp.Process(target=sac,
args=(rank, E, T,args, model_q[rank], buffer_q, torch.device("cpu"), tensorboard_dir))
p.start()
# time.sleep(5)
processes.append(p)
target_entropy = -np.log((1.0 / act_dim)) * 0.5
alpha = max(global_ac.log_alpha.exp().item(), args.min_alpha) if args.dynamic_alpha else args.min_alpha
# alpha = args.min_alpha
e = E.value()
while T.value() < args.episode:
t = T.value()
# If do experiments, need to block the receive if now new data, otherwise will impact the result
# if not buffer_q.empty():
# print("Going to read data from ACTOR...")
# before_rece = time.time()
received_data = buffer_q.get()
# wait_time = time.time() - before_rece
# print("waited {}s Reading data from ACTOR!!!".format(wait_time))
(trajectory, meta) = copy.deepcopy(received_data)
del received_data
if args.cpc and len(trajectory) <= args.timestep:
continue
# if meta[0][0] == "4":
# bufferopp1.store(trajectory, meta=meta)
# elif meta[0][0] == "5":
# bufferopp3.store(trajectory, meta=meta)
replay_buffer.store(trajectory, meta=meta)
writer.add_scalar("learner/buffer_size", replay_buffer.size, e)
# writer.add_scalar("learner/buffer1_size", bufferopp1.size, e)
# writer.add_scalar("learner/buffer2_size", bufferopp3.size, e)
E.increment()
e = E.value()
# SAC Update handling
if e >= args.update_after:
# if the batch size is very large, can train sac once per round
for _ in range(args.update_every):
T.increment()
t = T.value()
# batch1 = bufferopp1.sample_trans(int(args.batch_size*args.opp1_per), device=device)
# batch3 = bufferopp1.sample_trans(int(args.batch_size*args.opp3_per), device=device)
# batch = {k: torch.cat((batch1[k], batch3[k]), dim=0) for k, v in batch1.items()}
batch = replay_buffer.sample_trans(args.batch_size, device=device)
# First run one gradient descent step for Q1 and Q2
q1_optimizer.zero_grad()
q2_optimizer.zero_grad()
loss_q = global_ac.compute_loss_q(batch, global_ac_targ, args.gamma, alpha)
loss_q.backward()
nn.utils.clip_grad_norm_(global_ac.parameters(), max_norm=20, norm_type=2)
q1_optimizer.step()
q2_optimizer.step()
# Next run one gradient descent step for pi.
pi_optimizer.zero_grad()
loss_pi, entropy = global_ac.compute_loss_pi(batch, alpha)
loss_pi.backward()
nn.utils.clip_grad_norm_(global_ac.parameters(), max_norm=20, norm_type=2)
pi_optimizer.step()
alpha_optim.zero_grad()
alpha_loss = -(global_ac.log_alpha * (entropy + target_entropy).detach()).mean()
alpha_loss.backward(retain_graph=False)
nn.utils.clip_grad_norm_(global_ac.parameters(), max_norm=20, norm_type=2)
alpha = max(global_ac.log_alpha.exp().item(), args.min_alpha) if args.dynamic_alpha else args.min_alpha
alpha_optim.step()
# Finally, update target networks by polyak averaging.
with torch.no_grad():
for p, p_targ in zip(global_ac.parameters(), global_ac_targ.parameters()):
p_targ.data.copy_((1 - args.polyak) * p.data + args.polyak * p_targ.data)
writer.add_scalar("learner/pi_loss", loss_pi.detach().item(), t)
writer.add_scalar("learner/q_loss", loss_q.detach().item(), t)
writer.add_scalar("learner/alpha_loss", alpha_loss.detach().item(), t)
writer.add_scalar("learner/alpha", alpha, t)
writer.add_scalar("learner/entropy", entropy.detach().mean().item(), t)
# CPC update handing
if args.cpc and e > args.cpc_batch * 2 and e % args.cpc_update_freq == 0:
for _ in range(args.cpc_update_freq):
data, indexes, min_len = replay_buffer.sample_traj(args.cpc_batch)
cpc_optimizer.zero_grad()
c_hidden = global_cpc.init_hidden(len(data), args.c_dim)
acc, loss, latents = global_cpc(data, c_hidden)
# replay_buffer.update_latent(indexes, min_len, latents.detach())
loss.backward()
# add gradient clipping
nn.utils.clip_grad_norm_(global_cpc.parameters(), max_norm=20, norm_type=2)
cpc_optimizer.step()
writer.add_scalar("learner/cpc_acc", acc, t)
writer.add_scalar("learner/cpc_loss", loss.detach().item(), t)
# CPC latent update
# if args.cpc and e > args.cpc_batch and e % 500 == 0 and e != last_updated:
# replay_buffer.create_latents(e=e)
# last_updated = e
# deliver the model
if e % (args.n_process * 2) == 0 and e >= args.update_after and e != last_deliver:
temp = copy.deepcopy(global_ac).cpu()
shared_ac_state_dict = copy.deepcopy(temp.state_dict())
for i in range(args.opp_num, args.n_process + args.opp_num): # n is test model queue
model_q[i].put(shared_ac_state_dict, )
last_deliver = e
# evaluation model
if t > 0 and t % args.test_every == 0 and e >= args.update_after and test_t != t:
temp = copy.deepcopy(global_ac).cpu()
test_model = copy.deepcopy(temp.state_dict())
send_obj = (test_model, t)
# in case the large model state dict will make the queue full to stuck the training process
evaluation_queue.append(send_obj)
if any([model_q[i].empty() for i in range(args.opp_num)]):
temp = evaluation_queue.pop(0)
for i in range(args.opp_num):
model_q[i].put(temp, )
test_t = t
# save the model
if e % args.save_freq == 0 and e >= args.update_after and e != last_saved:
torch.save(global_ac.state_dict(), os.path.join(experiment_dir, args.model_para))
if args.cpc:
torch.save(global_cpc.state_dict(), os.path.join(experiment_dir, args.cpc_para))
# state_dict_trans(global_ac.state_dict(), os.path.join(experiment_dir, args.numpy_para))
# torch.save((e, t, list(scores), list(wins)), os.path.join(args.save_dir, args.exp_name, "model_data_{}".format(e)))
print("Saving model at episode:{}".format(e))
last_saved = e
print("after the training loop, waiting for the end of evaluation")
# consume all the queue to make sure all processes can be closed correctly
while not buffer_q.empty():
buffer_q.get()
while len(evaluation_queue) > 0:
if any([model_q[i].empty() for i in range(args.opp_num)]):
temp = evaluation_queue.pop(0)
for i in range(args.opp_num):
model_q[i].put(temp, )
for p in processes:
p.join()