#!/usr/bin/env python3
import argparse
import datetime
import os
import pickle
import pprint
import sys
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from examples.atari.atari_network import DQN
from examples.atari.atari_wrapper import make_atari_env
from examples.offline.utils import load_buffer
from tianshou.data import Collector, VectorReplayBuffer
from tianshou.policy import DiscreteBCQPolicy
from tianshou.trainer import OfflineTrainer
from tianshou.utils import TensorboardLogger, WandbLogger
from tianshou.utils.net.common import ActorCritic
from tianshou.utils.net.discrete import Actor
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--task", type=str, default="PongNoFrameskip-v4")
parser.add_argument("--seed", type=int, default=1626)
parser.add_argument("--eps-test", type=float, default=0.001)
parser.add_argument("--lr", type=float, default=6.25e-5)
parser.add_argument("--gamma", type=float, default=0.99)
parser.add_argument("--n-step", type=int, default=1)
parser.add_argument("--target-update-freq", type=int, default=8000)
parser.add_argument("--unlikely-action-threshold", type=float, default=0.3)
parser.add_argument("--imitation-logits-penalty", type=float, default=0.01)
parser.add_argument("--epoch", type=int, default=100)
parser.add_argument("--update-per-epoch", type=int, default=10000)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[512])
parser.add_argument("--test-num", type=int, default=10)
parser.add_argument("--frames-stack", type=int, default=4)
parser.add_argument("--scale-obs", type=int, default=0)
parser.add_argument("--logdir", type=str, default="log")
parser.add_argument("--render", type=float, default=0.0)
parser.add_argument("--resume-path", type=str, default=None)
parser.add_argument("--resume-id", type=str, default=None)
parser.add_argument(
"--logger",
type=str,
default="tensorboard",
choices=["tensorboard", "wandb"],
)
parser.add_argument("--wandb-project", type=str, default="offline_atari.benchmark")
parser.add_argument(
"--watch",
default=False,
action="store_true",
help="watch the play of pre-trained policy only",
)
parser.add_argument("--log-interval", type=int, default=100)
parser.add_argument(
"--load-buffer-name",
type=str,
default="./expert_DQN_PongNoFrameskip-v4.hdf5",
)
parser.add_argument("--buffer-from-rl-unplugged", action="store_true", default=False)
parser.add_argument(
"--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
)
return parser.parse_known_args()[0]
def test_discrete_bcq(args=get_args()):
# envs
env, _, test_envs = make_atari_env(
args.task,
args.seed,
1,
args.test_num,
scale=args.scale_obs,
frame_stack=args.frames_stack,
)
args.state_shape = env.observation_space.shape or env.observation_space.n
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)
# seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# model
feature_net = DQN(
*args.state_shape,
args.action_shape,
device=args.device,
features_only=True,
).to(args.device)
policy_net = Actor(
feature_net,
args.action_shape,
device=args.device,
hidden_sizes=args.hidden_sizes,
softmax_output=False,
).to(args.device)
imitation_net = Actor(
feature_net,
args.action_shape,
device=args.device,
hidden_sizes=args.hidden_sizes,
softmax_output=False,
).to(args.device)
actor_critic = ActorCritic(policy_net, imitation_net)
optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
# define policy
policy = DiscreteBCQPolicy(
model=policy_net,
imitator=imitation_net,
optim=optim,
action_space=env.action_space,
discount_factor=args.gamma,
estimation_step=args.n_step,
target_update_freq=args.target_update_freq,
eval_eps=args.eps_test,
unlikely_action_threshold=args.unlikely_action_threshold,
imitation_logits_penalty=args.imitation_logits_penalty,
)
# 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)
# buffer
if args.buffer_from_rl_unplugged:
buffer = load_buffer(args.load_buffer_name)
else:
assert os.path.exists(
args.load_buffer_name,
), "Please run atari_dqn.py first to get expert's data buffer."
if args.load_buffer_name.endswith(".pkl"):
with open(args.load_buffer_name, "rb") as f:
buffer = pickle.load(f)
elif args.load_buffer_name.endswith(".hdf5"):
buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name)
else:
print(f"Unknown buffer format: {args.load_buffer_name}")
sys.exit(0)
print("Replay buffer size:", len(buffer), flush=True)
# collector
test_collector = Collector(policy, test_envs, exploration_noise=True)
# log
now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
args.algo_name = "bcq"
log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
log_path = os.path.join(args.logdir, log_name)
# logger
if args.logger == "wandb":
logger = WandbLogger(
save_interval=1,
name=log_name.replace(os.path.sep, "__"),
run_id=args.resume_id,
config=args,
project=args.wandb_project,
)
writer = SummaryWriter(log_path)
writer.add_text("args", str(args))
if args.logger == "tensorboard":
logger = TensorboardLogger(writer)
else: # wandb
logger.load(writer)
def save_best_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
def stop_fn(mean_rewards):
return False
# watch agent's performance
def watch():
print("Setup test envs ...")
policy.eval()
policy.set_eps(args.eps_test)
test_envs.seed(args.seed)
print("Testing agent ...")
test_collector.reset()
result = test_collector.collect(n_episode=args.test_num, render=args.render)
pprint.pprint(result)
rew = result["rews"].mean()
print(f'Mean reward (over {result["n/ep"]} episodes): {rew}')
if args.watch:
watch()
sys.exit(0)
result = OfflineTrainer(
policy=policy,
buffer=buffer,
test_collector=test_collector,
max_epoch=args.epoch,
step_per_epoch=args.update_per_epoch,
episode_per_test=args.test_num,
batch_size=args.batch_size,
stop_fn=stop_fn,
save_best_fn=save_best_fn,
logger=logger,
).run()
pprint.pprint(result)
watch()
if __name__ == "__main__":
test_discrete_bcq(get_args())