8a5e2190f7
- rename BasicLogger to TensorboardLogger - refactor logger code - add WandbLogger Co-authored-by: Jiayi Weng <trinkle23897@gmail.com>
178 lines
7.3 KiB
Python
178 lines
7.3 KiB
Python
import os
|
|
import torch
|
|
import pprint
|
|
import argparse
|
|
import numpy as np
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.policy import C51Policy
|
|
from tianshou.utils import TensorboardLogger
|
|
from tianshou.env import SubprocVectorEnv
|
|
from tianshou.trainer import offpolicy_trainer
|
|
from tianshou.data import Collector, VectorReplayBuffer
|
|
|
|
from env import Env
|
|
from network import C51
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--task', type=str, default='D1_basic')
|
|
parser.add_argument('--seed', type=int, default=0)
|
|
parser.add_argument('--eps-test', type=float, default=0.005)
|
|
parser.add_argument('--eps-train', type=float, default=1.)
|
|
parser.add_argument('--eps-train-final', type=float, default=0.05)
|
|
parser.add_argument('--buffer-size', type=int, default=2000000)
|
|
parser.add_argument('--lr', type=float, default=0.0001)
|
|
parser.add_argument('--gamma', type=float, default=0.99)
|
|
parser.add_argument('--num-atoms', type=int, default=51)
|
|
parser.add_argument('--v-min', type=float, default=-10.)
|
|
parser.add_argument('--v-max', type=float, default=10.)
|
|
parser.add_argument('--n-step', type=int, default=3)
|
|
parser.add_argument('--target-update-freq', type=int, default=500)
|
|
parser.add_argument('--epoch', type=int, default=300)
|
|
parser.add_argument('--step-per-epoch', type=int, default=100000)
|
|
parser.add_argument('--step-per-collect', type=int, default=10)
|
|
parser.add_argument('--update-per-step', type=float, default=0.1)
|
|
parser.add_argument('--batch-size', type=int, default=64)
|
|
parser.add_argument('--training-num', type=int, default=10)
|
|
parser.add_argument('--test-num', type=int, default=100)
|
|
parser.add_argument('--logdir', type=str, default='log')
|
|
parser.add_argument('--render', type=float, default=0.)
|
|
parser.add_argument(
|
|
'--device', type=str,
|
|
default='cuda' if torch.cuda.is_available() else 'cpu')
|
|
parser.add_argument('--frames-stack', type=int, default=4)
|
|
parser.add_argument('--skip-num', type=int, default=4)
|
|
parser.add_argument('--resume-path', type=str, default=None)
|
|
parser.add_argument('--watch', default=False, action='store_true',
|
|
help='watch the play of pre-trained policy only')
|
|
parser.add_argument('--save-lmp', default=False, action='store_true',
|
|
help='save lmp file for replay whole episode')
|
|
parser.add_argument('--save-buffer-name', type=str, default=None)
|
|
return parser.parse_args()
|
|
|
|
|
|
def test_c51(args=get_args()):
|
|
args.cfg_path = f"maps/{args.task}.cfg"
|
|
args.wad_path = f"maps/{args.task}.wad"
|
|
args.res = (args.skip_num, 84, 84)
|
|
env = Env(args.cfg_path, args.frames_stack, args.res)
|
|
args.state_shape = args.res
|
|
args.action_shape = env.action_space.shape or env.action_space.n
|
|
# should be N_FRAMES x H x W
|
|
print("Observations shape:", args.state_shape)
|
|
print("Actions shape:", args.action_shape)
|
|
# make environments
|
|
train_envs = SubprocVectorEnv([
|
|
lambda: Env(args.cfg_path, args.frames_stack, args.res)
|
|
for _ in range(args.training_num)])
|
|
test_envs = SubprocVectorEnv([
|
|
lambda: Env(args.cfg_path, args.frames_stack,
|
|
args.res, args.save_lmp)
|
|
for _ in range(min(os.cpu_count() - 1, args.test_num))])
|
|
# seed
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
train_envs.seed(args.seed)
|
|
test_envs.seed(args.seed)
|
|
# define model
|
|
net = C51(*args.state_shape, args.action_shape,
|
|
args.num_atoms, args.device)
|
|
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
|
|
# define policy
|
|
policy = C51Policy(
|
|
net, optim, args.gamma, args.num_atoms, args.v_min, args.v_max,
|
|
args.n_step, target_update_freq=args.target_update_freq
|
|
).to(args.device)
|
|
# load a previous policy
|
|
if args.resume_path:
|
|
policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
|
|
print("Loaded agent from: ", args.resume_path)
|
|
# replay buffer: `save_last_obs` and `stack_num` can be removed together
|
|
# when you have enough RAM
|
|
buffer = VectorReplayBuffer(
|
|
args.buffer_size, buffer_num=len(train_envs), ignore_obs_next=True,
|
|
save_only_last_obs=True, stack_num=args.frames_stack)
|
|
# collector
|
|
train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
|
|
test_collector = Collector(policy, test_envs, exploration_noise=True)
|
|
# log
|
|
log_path = os.path.join(args.logdir, args.task, 'c51')
|
|
writer = SummaryWriter(log_path)
|
|
writer.add_text("args", str(args))
|
|
logger = TensorboardLogger(writer)
|
|
|
|
def save_fn(policy):
|
|
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
|
|
|
|
def stop_fn(mean_rewards):
|
|
if env.spec.reward_threshold:
|
|
return mean_rewards >= env.spec.reward_threshold
|
|
elif 'Pong' in args.task:
|
|
return mean_rewards >= 20
|
|
else:
|
|
return False
|
|
|
|
def train_fn(epoch, env_step):
|
|
# nature DQN setting, linear decay in the first 1M steps
|
|
if env_step <= 1e6:
|
|
eps = args.eps_train - env_step / 1e6 * \
|
|
(args.eps_train - args.eps_train_final)
|
|
else:
|
|
eps = args.eps_train_final
|
|
policy.set_eps(eps)
|
|
logger.write('train/eps', env_step, eps)
|
|
|
|
def test_fn(epoch, env_step):
|
|
policy.set_eps(args.eps_test)
|
|
|
|
# watch agent's performance
|
|
def watch():
|
|
print("Setup test envs ...")
|
|
policy.eval()
|
|
policy.set_eps(args.eps_test)
|
|
test_envs.seed(args.seed)
|
|
if args.save_buffer_name:
|
|
print(f"Generate buffer with size {args.buffer_size}")
|
|
buffer = VectorReplayBuffer(
|
|
args.buffer_size, buffer_num=len(test_envs),
|
|
ignore_obs_next=True, save_only_last_obs=True,
|
|
stack_num=args.frames_stack)
|
|
collector = Collector(policy, test_envs, buffer,
|
|
exploration_noise=True)
|
|
result = collector.collect(n_step=args.buffer_size)
|
|
print(f"Save buffer into {args.save_buffer_name}")
|
|
# Unfortunately, pickle will cause oom with 1M buffer size
|
|
buffer.save_hdf5(args.save_buffer_name)
|
|
else:
|
|
print("Testing agent ...")
|
|
test_collector.reset()
|
|
result = test_collector.collect(n_episode=args.test_num,
|
|
render=args.render)
|
|
rew = result["rews"].mean()
|
|
lens = result["lens"].mean() * args.skip_num
|
|
print(f'Mean reward (over {result["n/ep"]} episodes): {rew}')
|
|
print(f'Mean length (over {result["n/ep"]} episodes): {lens}')
|
|
|
|
if args.watch:
|
|
watch()
|
|
exit(0)
|
|
|
|
# test train_collector and start filling replay buffer
|
|
train_collector.collect(n_step=args.batch_size * args.training_num)
|
|
# trainer
|
|
result = offpolicy_trainer(
|
|
policy, train_collector, test_collector, args.epoch,
|
|
args.step_per_epoch, args.step_per_collect, args.test_num,
|
|
args.batch_size, train_fn=train_fn, test_fn=test_fn,
|
|
stop_fn=stop_fn, save_fn=save_fn, logger=logger,
|
|
update_per_step=args.update_per_step, test_in_train=False)
|
|
|
|
pprint.pprint(result)
|
|
watch()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_c51(get_args())
|