def __init__(
self,
feature_net: nn.Module,
feature_dim: int,
action_dim: int,
hidden_sizes: Sequence[int] = (),
device: Union[str, torch.device] = "cpu"
) -> None:
super().__init__()
self.feature_net = feature_net
self.forward_model = MLP(
feature_dim + action_dim,
output_dim=feature_dim,
hidden_sizes=hidden_sizes,
device=device
)
self.inverse_model = MLP(
feature_dim * 2,
output_dim=action_dim,
hidden_sizes=hidden_sizes,
device=device
)
self.feature_dim = feature_dim
self.action_dim = action_dim
self.device = device
def __init__(
self,
preprocess_net: nn.Module,
action_shape: Sequence[int],
hidden_sizes: Sequence[int] = (),
max_action: float = 1.0,
device: Union[str, int, torch.device] = "cpu",
unbounded: bool = False,
conditioned_sigma: bool = False,
preprocess_net_output_dim: Optional[int] = None,
) -> None:
super().__init__()
self.preprocess = preprocess_net
self.device = device
self.output_dim = int(np.prod(action_shape))
input_dim = getattr(preprocess_net, "output_dim",
preprocess_net_output_dim)
self.mu = MLP(input_dim,
self.output_dim,
hidden_sizes,
device=self.device)
self._c_sigma = conditioned_sigma
if conditioned_sigma:
self.sigma = MLP(input_dim,
self.output_dim,
hidden_sizes,
device=self.device)
else:
self.sigma_param = nn.Parameter(torch.zeros(self.output_dim, 1))
self._max = max_action
self._unbounded = unbounded
def test_net():
# here test the networks that does not appear in the other script
bsz = 64
# MLP
data = torch.rand([bsz, 3])
mlp = MLP(3, 6, hidden_sizes=[128])
assert list(mlp(data).shape) == [bsz, 6]
# output == 0 and len(hidden_sizes) == 0 means identity model
mlp = MLP(6, 0)
assert data.shape == mlp(data).shape
# common net
state_shape = (10, 2)
action_shape = (5, )
data = torch.rand([bsz, *state_shape])
expect_output_shape = [bsz, *action_shape]
net = Net(
state_shape,
action_shape,
hidden_sizes=[128, 128],
norm_layer=torch.nn.LayerNorm,
activation=None
)
assert list(net(data)[0].shape) == expect_output_shape
assert str(net).count("LayerNorm") == 2
assert str(net).count("ReLU") == 0
Q_param = V_param = {"hidden_sizes": [128, 128]}
net = Net(
state_shape,
action_shape,
hidden_sizes=[128, 128],
dueling_param=(Q_param, V_param)
)
assert list(net(data)[0].shape) == expect_output_shape
# concat
net = Net(state_shape, action_shape, hidden_sizes=[128], concat=True)
data = torch.rand([bsz, np.prod(state_shape) + np.prod(action_shape)])
expect_output_shape = [bsz, 128]
assert list(net(data)[0].shape) == expect_output_shape
net = Net(
state_shape,
action_shape,
hidden_sizes=[128],
concat=True,
dueling_param=(Q_param, V_param)
)
assert list(net(data)[0].shape) == expect_output_shape
# recurrent actor/critic
data = torch.rand([bsz, *state_shape]).flatten(1)
expect_output_shape = [bsz, *action_shape]
net = RecurrentActorProb(3, state_shape, action_shape)
mu, sigma = net(data)[0]
assert mu.shape == sigma.shape
assert list(mu.shape) == [bsz, 5]
net = RecurrentCritic(3, state_shape, action_shape)
data = torch.rand([bsz, 8, np.prod(state_shape)])
act = torch.rand(expect_output_shape)
assert list(net(data, act).shape) == [bsz, 1]
def __init__(
self,
preprocess_net: nn.Module,
hidden_sizes: Sequence[int] = (),
device: Union[str, int, torch.device] = "cpu",
preprocess_net_output_dim: Optional[int] = None,
) -> None:
super().__init__()
self.device = device
self.preprocess = preprocess_net
self.output_dim = 1
input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
self.last = MLP(input_dim, 1, hidden_sizes, device=self.device)
def __init__(
self,
preprocess_net: nn.Module,
hidden_sizes: Sequence[int] = (),
last_size: int = 1,
preprocess_net_output_dim: Optional[int] = None,
) -> None:
super().__init__()
self.preprocess = preprocess_net
self.output_dim = last_size
input_dim = getattr(preprocess_net, "output_dim",
preprocess_net_output_dim)
self.last = MLP(input_dim, last_size, hidden_sizes)
def __init__(
self,
preprocess_net: nn.Module,
action_shape: Sequence[int],
hidden_sizes: Sequence[int] = (),
softmax_output: bool = True,
preprocess_net_output_dim: Optional[int] = None,
) -> None:
super().__init__()
self.preprocess = preprocess_net
self.output_dim = np.prod(action_shape)
input_dim = getattr(preprocess_net, "output_dim",
preprocess_net_output_dim)
self.last = MLP(input_dim, self.output_dim, hidden_sizes)
self.softmax_output = softmax_output
def __init__(
self,
preprocess_net: nn.Module,
action_shape: Sequence[int],
hidden_sizes: Sequence[int] = (),
max_action: float = 1.0,
device: Union[str, int, torch.device] = "cpu",
preprocess_net_output_dim: Optional[int] = None,
) -> None:
super().__init__()
self.device = device
self.preprocess = preprocess_net
self.output_dim = int(np.prod(action_shape))
input_dim = getattr(preprocess_net, "output_dim", preprocess_net_output_dim)
self.last = MLP(input_dim, self.output_dim, hidden_sizes, device=self.device)
self._max = max_action
def __init__(self,
state_shape: Union[int, Sequence[int]],
action_shape: Union[int, Sequence[int]] = 0,
hidden_sizes: Sequence[int] = (256, 256),
activation: Optional[ModuleType] = nn.ReLU,
device: Union[str, int, torch.device] = "cpu") -> None:
super().__init__()
self.device = device
input_dim = int(np.prod(state_shape))
action_dim = int(np.prod(action_shape))
self.net = MLP(input_dim,
action_dim,
hidden_sizes,
norm_layer=None,
activation=activation,
device=device)
self.output_dim = self.net.output_dim
def __init__(
self,
preprocess_net: nn.Module, # bu bizim dilated net
action_shape: Sequence[int],
hidden_sizes: Sequence[int] = (),
softmax_output: bool = True,
preprocess_net_output_dim: Optional[int] = None,
device: Union[str, int, torch.device] = "cpu",
) -> None:
super().__init__()
self.device = device
self.preprocess = preprocess_net
self.output_dim = int(np.prod(action_shape))
input_dim = getattr(preprocess_net, "output_dim",
preprocess_net_output_dim)
self.last = MLP(input_dim,
self.output_dim,
hidden_sizes,
device=self.device)
self.softmax_output = softmax_output
def __init__(
self,
preprocess_net: nn.Module,
hidden_sizes: Sequence[int] = (),
device: Union[str, int, torch.device] = "cpu",
preprocess_net_output_dim: Optional[int] = None,
linear_layer: Type[nn.Linear] = nn.Linear,
flatten_input: bool = True,
) -> None:
super().__init__()
self.device = device
self.preprocess = preprocess_net
self.output_dim = 1
input_dim = getattr(preprocess_net, "output_dim",
preprocess_net_output_dim)
self.last = MLP(
input_dim, # type: ignore
1,
hidden_sizes,
device=self.device,
linear_layer=linear_layer,
flatten_input=flatten_input,
)
def test_bcq(args=get_args()):
if os.path.exists(args.load_buffer_name) and os.path.isfile(args.load_buffer_name):
if args.load_buffer_name.endswith(".hdf5"):
buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name)
else:
buffer = pickle.load(open(args.load_buffer_name, "rb"))
else:
buffer = gather_data()
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
args.max_action = env.action_space.high[0] # float
if args.reward_threshold is None:
# too low?
default_reward_threshold = {"Pendulum-v0": -1100, "Pendulum-v1": -1100}
args.reward_threshold = default_reward_threshold.get(
args.task, env.spec.reward_threshold
)
args.state_dim = args.state_shape[0]
args.action_dim = args.action_shape[0]
# 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)
test_envs.seed(args.seed)
# model
# perturbation network
net_a = MLP(
input_dim=args.state_dim + args.action_dim,
output_dim=args.action_dim,
hidden_sizes=args.hidden_sizes,
device=args.device,
)
actor = Perturbation(
net_a, max_action=args.max_action, device=args.device, phi=args.phi
).to(args.device)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
net_c1 = Net(
args.state_shape,
args.action_shape,
hidden_sizes=args.hidden_sizes,
concat=True,
device=args.device,
)
net_c2 = Net(
args.state_shape,
args.action_shape,
hidden_sizes=args.hidden_sizes,
concat=True,
device=args.device,
)
critic1 = Critic(net_c1, device=args.device).to(args.device)
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
critic2 = Critic(net_c2, device=args.device).to(args.device)
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
# vae
# output_dim = 0, so the last Module in the encoder is ReLU
vae_encoder = MLP(
input_dim=args.state_dim + args.action_dim,
hidden_sizes=args.vae_hidden_sizes,
device=args.device,
)
if not args.latent_dim:
args.latent_dim = args.action_dim * 2
vae_decoder = MLP(
input_dim=args.state_dim + args.latent_dim,
output_dim=args.action_dim,
hidden_sizes=args.vae_hidden_sizes,
device=args.device,
)
vae = VAE(
vae_encoder,
vae_decoder,
hidden_dim=args.vae_hidden_sizes[-1],
latent_dim=args.latent_dim,
max_action=args.max_action,
device=args.device,
).to(args.device)
vae_optim = torch.optim.Adam(vae.parameters())
policy = BCQPolicy(
actor,
actor_optim,
critic1,
critic1_optim,
critic2,
critic2_optim,
vae,
vae_optim,
device=args.device,
gamma=args.gamma,
tau=args.tau,
lmbda=args.lmbda,
)
# 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)
# collector
# buffer has been gathered
# train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
test_collector = Collector(policy, test_envs)
# log
t0 = datetime.datetime.now().strftime("%m%d_%H%M%S")
log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_bcq'
log_path = os.path.join(args.logdir, args.task, 'bcq', log_file)
writer = SummaryWriter(log_path)
writer.add_text("args", str(args))
logger = TensorboardLogger(writer)
def save_best_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 watch():
policy.load_state_dict(
torch.load(
os.path.join(log_path, 'policy.pth'), map_location=torch.device('cpu')
)
)
policy.eval()
collector = Collector(policy, env)
collector.collect(n_episode=1, render=1 / 35)
# trainer
result = offline_trainer(
policy,
buffer,
test_collector,
args.epoch,
args.step_per_epoch,
args.test_num,
args.batch_size,
save_best_fn=save_best_fn,
stop_fn=stop_fn,
logger=logger,
)
assert stop_fn(result['best_reward'])
# Let's watch its performance!
if __name__ == '__main__':
pprint.pprint(result)
env = gym.make(args.task)
policy.eval()
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()}")
def test_ppo(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)
# model
net = Net(args.state_shape,
hidden_sizes=args.hidden_sizes,
device=args.device)
actor = Actor(net, args.action_shape, device=args.device).to(args.device)
critic = Critic(net, device=args.device).to(args.device)
actor_critic = ActorCritic(actor, critic)
# orthogonal initialization
for m in actor_critic.modules():
if isinstance(m, torch.nn.Linear):
torch.nn.init.orthogonal_(m.weight)
torch.nn.init.zeros_(m.bias)
optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
dist = torch.distributions.Categorical
policy = PPOPolicy(actor,
critic,
optim,
dist,
discount_factor=args.gamma,
max_grad_norm=args.max_grad_norm,
eps_clip=args.eps_clip,
vf_coef=args.vf_coef,
ent_coef=args.ent_coef,
gae_lambda=args.gae_lambda,
reward_normalization=args.rew_norm,
dual_clip=args.dual_clip,
value_clip=args.value_clip,
action_space=env.action_space,
deterministic_eval=True,
advantage_normalization=args.norm_adv,
recompute_advantage=args.recompute_adv)
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)
# collector
train_collector = Collector(
policy, train_envs,
VectorReplayBuffer(args.buffer_size, len(train_envs)))
test_collector = Collector(policy, test_envs)
# log
log_path = os.path.join(args.logdir, args.task, 'ppo_icm')
writer = SummaryWriter(log_path)
logger = TensorboardLogger(writer)
def save_best_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
# trainer
result = onpolicy_trainer(policy,
train_collector,
test_collector,
args.epoch,
args.step_per_epoch,
args.repeat_per_collect,
args.test_num,
args.batch_size,
step_per_collect=args.step_per_collect,
stop_fn=stop_fn,
save_best_fn=save_best_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()
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()}")
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_best_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_best_fn=save_best_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()}")
def test_bcq():
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
args.max_action = env.action_space.high[0] # float
print("device:", args.device)
print("Observations shape:", args.state_shape)
print("Actions shape:", args.action_shape)
print("Action range:", np.min(env.action_space.low),
np.max(env.action_space.high))
args.state_dim = args.state_shape[0]
args.action_dim = args.action_shape[0]
print("Max_action", args.max_action)
# test_envs = gym.make(args.task)
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)
test_envs.seed(args.seed)
# model
# perturbation network
net_a = MLP(
input_dim=args.state_dim + args.action_dim,
output_dim=args.action_dim,
hidden_sizes=args.hidden_sizes,
device=args.device,
)
actor = Perturbation(net_a,
max_action=args.max_action,
device=args.device,
phi=args.phi).to(args.device)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
net_c1 = Net(
args.state_shape,
args.action_shape,
hidden_sizes=args.hidden_sizes,
concat=True,
device=args.device,
)
net_c2 = Net(
args.state_shape,
args.action_shape,
hidden_sizes=args.hidden_sizes,
concat=True,
device=args.device,
)
critic1 = Critic(net_c1, device=args.device).to(args.device)
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
critic2 = Critic(net_c2, device=args.device).to(args.device)
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
# vae
# output_dim = 0, so the last Module in the encoder is ReLU
vae_encoder = MLP(
input_dim=args.state_dim + args.action_dim,
hidden_sizes=args.vae_hidden_sizes,
device=args.device,
)
if not args.latent_dim:
args.latent_dim = args.action_dim * 2
vae_decoder = MLP(
input_dim=args.state_dim + args.latent_dim,
output_dim=args.action_dim,
hidden_sizes=args.vae_hidden_sizes,
device=args.device,
)
vae = VAE(
vae_encoder,
vae_decoder,
hidden_dim=args.vae_hidden_sizes[-1],
latent_dim=args.latent_dim,
max_action=args.max_action,
device=args.device,
).to(args.device)
vae_optim = torch.optim.Adam(vae.parameters())
policy = BCQPolicy(
actor,
actor_optim,
critic1,
critic1_optim,
critic2,
critic2_optim,
vae,
vae_optim,
device=args.device,
gamma=args.gamma,
tau=args.tau,
lmbda=args.lmbda,
)
# 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)
# collector
test_collector = Collector(policy, test_envs)
# 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 watch():
if args.resume_path is None:
args.resume_path = os.path.join(log_path, "policy.pth")
policy.load_state_dict(
torch.load(args.resume_path, map_location=torch.device("cpu")))
policy.eval()
collector = Collector(policy, env)
collector.collect(n_episode=1, render=1 / 35)
if not args.watch:
dataset = d4rl.qlearning_dataset(gym.make(args.expert_data_task))
dataset_size = dataset["rewards"].size
print("dataset_size", dataset_size)
replay_buffer = ReplayBuffer(dataset_size)
for i in range(dataset_size):
replay_buffer.add(
Batch(
obs=dataset["observations"][i],
act=dataset["actions"][i],
rew=dataset["rewards"][i],
done=dataset["terminals"][i],
obs_next=dataset["next_observations"][i],
))
print("dataset loaded")
# trainer
result = offline_trainer(
policy,
replay_buffer,
test_collector,
args.epoch,
args.step_per_epoch,
args.test_num,
args.batch_size,
save_best_fn=save_best_fn,
logger=logger,
)
pprint.pprint(result)
else:
watch()
# Let's watch its performance!
policy.eval()
test_envs.seed(args.seed)
test_collector.reset()
result = test_collector.collect(n_episode=args.test_num,
render=args.render)
print(
f"Final reward: {result['rews'].mean()}, length: {result['lens'].mean()}"
)
