def test_discrete_sac(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
train_envs = SubprocVectorEnv(
[lambda: gym.make(args.task) for _ in range(args.training_num)])
test_envs = SubprocVectorEnv(
[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)
# model
net = Net(args.state_shape,
hidden_sizes=args.hidden_sizes,
device=args.device)
actor = Actor(net,
args.action_shape,
softmax_output=False,
device=args.device).to(args.device)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
net_c1 = Net(args.state_shape,
hidden_sizes=args.hidden_sizes,
device=args.device)
critic1 = Critic(net_c1, last_size=args.action_shape,
device=args.device).to(args.device)
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
net_c2 = Net(args.state_shape,
hidden_sizes=args.hidden_sizes,
device=args.device)
critic2 = Critic(net_c2, last_size=args.action_shape,
device=args.device).to(args.device)
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
# better not to use auto alpha in CartPole
if args.auto_alpha:
target_entropy = 0.98 * np.log(np.prod(args.action_shape))
log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
args.alpha = (target_entropy, log_alpha, alpha_optim)
policy = DiscreteSACPolicy(actor,
actor_optim,
critic1,
critic1_optim,
critic2,
critic2_optim,
args.tau,
args.gamma,
args.alpha,
reward_normalization=args.rew_norm,
ignore_done=args.ignore_done)
# collector
train_collector = Collector(policy, train_envs,
ReplayBuffer(args.buffer_size))
test_collector = Collector(policy, test_envs)
# train_collector.collect(n_step=args.buffer_size)
# log
log_path = os.path.join(args.logdir, args.task, 'discrete_sac')
writer = SummaryWriter(log_path)
def save_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
def stop_fn(mean_rewards):
return mean_rewards >= env.spec.reward_threshold
# trainer
result = offpolicy_trainer(policy,
train_collector,
test_collector,
args.epoch,
args.step_per_epoch,
args.collect_per_step,
args.test_num,
args.batch_size,
stop_fn=stop_fn,
save_fn=save_fn,
writer=writer,
test_in_train=False)
assert stop_fn(result['best_reward'])
if __name__ == '__main__':
pprint.pprint(result)
# Let's watch its performance!
env = gym.make(args.task)
policy.eval()
collector = Collector(policy, env)
result = collector.collect(n_episode=1, render=args.render)
print(f'Final reward: {result["rew"]}, length: {result["len"]}')
def test_discrete_sac(args=get_args()):
env, train_envs, test_envs = make_atari_env(
args.task,
args.seed,
args.training_num,
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)
# define model
net = DQN(*args.state_shape,
args.action_shape,
device=args.device,
features_only=True,
output_dim=args.hidden_size)
actor = Actor(net,
args.action_shape,
device=args.device,
softmax_output=False)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
critic1 = Critic(net, last_size=args.action_shape, device=args.device)
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
critic2 = Critic(net, last_size=args.action_shape, device=args.device)
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
# define policy
if args.auto_alpha:
target_entropy = 0.98 * np.log(np.prod(args.action_shape))
log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
args.alpha = (target_entropy, log_alpha, alpha_optim)
policy = DiscreteSACPolicy(
actor,
actor_optim,
critic1,
critic1_optim,
critic2,
critic2_optim,
args.tau,
args.gamma,
args.alpha,
estimation_step=args.n_step,
reward_normalization=args.rew_norm,
).to(args.device)
if args.icm_lr_scale > 0:
feature_net = DQN(*args.state_shape,
args.action_shape,
args.device,
features_only=True)
action_dim = np.prod(args.action_shape)
feature_dim = feature_net.output_dim
icm_net = IntrinsicCuriosityModule(
feature_net.net,
feature_dim,
action_dim,
hidden_sizes=[args.hidden_size],
device=args.device,
)
icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.actor_lr)
policy = ICMPolicy(policy, icm_net, icm_optim, args.icm_lr_scale,
args.icm_reward_scale,
args.icm_forward_loss_weight).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
now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
args.algo_name = "discrete_sac_icm" if args.icm_lr_scale > 0 else "discrete_sac"
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):
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 save_checkpoint_fn(epoch, env_step, gradient_step):
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
ckpt_path = os.path.join(log_path, "checkpoint.pth")
torch.save({"model": policy.state_dict()}, ckpt_path)
return ckpt_path
# watch agent's performance
def watch():
print("Setup test envs ...")
policy.eval()
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()
print(f"Mean reward (over {result['n/ep']} episodes): {rew}")
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,
stop_fn=stop_fn,
save_best_fn=save_best_fn,
logger=logger,
update_per_step=args.update_per_step,
test_in_train=False,
resume_from_log=args.resume_id is not None,
save_checkpoint_fn=save_checkpoint_fn,
)
pprint.pprint(result)
watch()