import argparse
import os
import pprint
import gym
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.policy import DQNPolicy, ICMPolicy
from tianshou.trainer import offpolicy_trainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import MLP, Net
from tianshou.utils.net.discrete import IntrinsicCuriosityModule
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='CartPole-v0')
parser.add_argument('--reward-threshold', type=float, default=None)
parser.add_argument('--seed', type=int, default=1626)
parser.add_argument('--eps-test', type=float, default=0.05)
parser.add_argument('--eps-train', type=float, default=0.1)
parser.add_argument('--buffer-size', type=int, default=20000)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--gamma', type=float, default=0.9)
parser.add_argument('--n-step', type=int, default=3)
parser.add_argument('--target-update-freq', type=int, default=320)
parser.add_argument('--epoch', type=int, default=20)
parser.add_argument('--step-per-epoch', type=int, default=10000)
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(
'--hidden-sizes', type=int, nargs='*', default=[128, 128, 128, 128]
)
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('--prioritized-replay', action="store_true", default=False)
parser.add_argument('--alpha', type=float, default=0.6)
parser.add_argument('--beta', type=float, default=0.4)
parser.add_argument(
'--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
)
parser.add_argument(
'--lr-scale',
type=float,
default=1.,
help='use intrinsic curiosity module with this lr scale'
)
parser.add_argument(
'--reward-scale',
type=float,
default=0.01,
help='scaling factor for intrinsic curiosity reward'
)
parser.add_argument(
'--forward-loss-weight',
type=float,
default=0.2,
help='weight for the forward model loss in ICM'
)
args = parser.parse_known_args()[0]
return args
def test_dqn_icm(args=get_args()):
env = gym.make(args.task)
args.state_shape = env.observation_space.shape or env.observation_space.n
args.action_shape = env.action_space.shape or env.action_space.n
if args.reward_threshold is None:
default_reward_threshold = {"CartPole-v0": 195}
args.reward_threshold = default_reward_threshold.get(
args.task, env.spec.reward_threshold
)
# train_envs = gym.make(args.task)
# you can also use tianshou.env.SubprocVectorEnv
train_envs = DummyVectorEnv(
[lambda: gym.make(args.task) for _ in range(args.training_num)]
)
# test_envs = gym.make(args.task)
test_envs = DummyVectorEnv(
[lambda: gym.make(args.task) for _ in range(args.test_num)]
)
# seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
train_envs.seed(args.seed)
test_envs.seed(args.seed)
# Q_param = V_param = {"hidden_sizes": [128]}
# model
net = Net(
args.state_shape,
args.action_shape,
hidden_sizes=args.hidden_sizes,
device=args.device,
# dueling=(Q_param, V_param),
).to(args.device)
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
policy = DQNPolicy(
net,
optim,
args.gamma,
args.n_step,
target_update_freq=args.target_update_freq,
)
feature_dim = args.hidden_sizes[-1]
feature_net = MLP(
np.prod(args.state_shape),
output_dim=feature_dim,
hidden_sizes=args.hidden_sizes[:-1],
device=args.device
)
action_dim = np.prod(args.action_shape)
icm_net = IntrinsicCuriosityModule(
feature_net,
feature_dim,
action_dim,
hidden_sizes=args.hidden_sizes[-1:],
device=args.device
).to(args.device)
icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)
policy = ICMPolicy(
policy, icm_net, icm_optim, args.lr_scale, args.reward_scale,
args.forward_loss_weight
)
# buffer
if args.prioritized_replay:
buf = PrioritizedVectorReplayBuffer(
args.buffer_size,
buffer_num=len(train_envs),
alpha=args.alpha,
beta=args.beta,
)
else:
buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
# collector
train_collector = Collector(policy, train_envs, buf, exploration_noise=True)
test_collector = Collector(policy, test_envs, exploration_noise=True)
# policy.set_eps(1)
train_collector.collect(n_step=args.batch_size * args.training_num)
# log
log_path = os.path.join(args.logdir, args.task, 'dqn_icm')
writer = SummaryWriter(log_path)
logger = TensorboardLogger(writer)
def save_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
def stop_fn(mean_rewards):
return mean_rewards >= args.reward_threshold
def train_fn(epoch, env_step):
# eps annnealing, just a demo
if env_step <= 10000:
policy.set_eps(args.eps_train)
elif env_step <= 50000:
eps = args.eps_train - (env_step - 10000) / \
40000 * (0.9 * args.eps_train)
policy.set_eps(eps)
else:
policy.set_eps(0.1 * args.eps_train)
def test_fn(epoch, env_step):
policy.set_eps(args.eps_test)
# 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,
update_per_step=args.update_per_step,
train_fn=train_fn,
test_fn=test_fn,
stop_fn=stop_fn,
save_fn=save_fn,
logger=logger,
)
assert stop_fn(result['best_reward'])
if __name__ == '__main__':
pprint.pprint(result)
# Let's watch its performance!
env = gym.make(args.task)
policy.eval()
policy.set_eps(args.eps_test)
collector = Collector(policy, env)
result = collector.collect(n_episode=1, render=args.render)
rews, lens = result["rews"], result["lens"]
print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
if __name__ == '__main__':
test_dqn_icm(get_args())