b900fdf6f2
Closes #947 This removes all kwargs from all policy constructors. While doing that, I also improved several names and added a whole lot of TODOs. ## Functional changes: 1. Added possibility to pass None as `critic2` and `critic2_optim`. In fact, the default behavior then should cover the absolute majority of cases 2. Added a function called `clone_optimizer` as a temporary measure to support passing `critic2_optim=None` ## Breaking changes: 1. `action_space` is no longer optional. In fact, it already was non-optional, as there was a ValueError in BasePolicy.init. So now several examples were fixed to reflect that 2. `reward_normalization` removed from DDPG and children. It was never allowed to pass it as `True` there, an error would have been raised in `compute_n_step_reward`. Now I removed it from the interface 3. renamed `critic1` and similar to `critic`, in order to have uniform interfaces. Note that the `critic` in DDPG was optional for the sole reason that child classes used `critic1`. I removed this optionality (DDPG can't do anything with `critic=None`) 4. Several renamings of fields (mostly private to public, so backwards compatible) ## Additional changes: 1. Removed type and default declaration from docstring. This kind of duplication is really not necessary 2. Policy constructors are now only called using named arguments, not a fragile mixture of positional and named as before 5. Minor beautifications in typing and code 6. Generally shortened docstrings and made them uniform across all policies (hopefully) ## Comment: With these changes, several problems in tianshou's inheritance hierarchy become more apparent. I tried highlighting them for future work. --------- Co-authored-by: Dominik Jain <d.jain@appliedai.de>
374 lines
11 KiB
Python
374 lines
11 KiB
Python
#!/usr/bin/env python3
|
|
|
|
import argparse
|
|
import datetime
|
|
import os
|
|
import pprint
|
|
|
|
import gymnasium as gym
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from examples.offline.utils import load_buffer_d4rl
|
|
from tianshou.data import Collector
|
|
from tianshou.env import SubprocVectorEnv
|
|
from tianshou.policy import CQLPolicy
|
|
from tianshou.trainer import OfflineTrainer
|
|
from tianshou.utils import TensorboardLogger, WandbLogger
|
|
from tianshou.utils.net.common import Net
|
|
from tianshou.utils.net.continuous import ActorProb, Critic
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--task",
|
|
type=str,
|
|
default="Hopper-v2",
|
|
help="The name of the OpenAI Gym environment to train on.",
|
|
)
|
|
parser.add_argument(
|
|
"--seed",
|
|
type=int,
|
|
default=0,
|
|
help="The random seed to use.",
|
|
)
|
|
parser.add_argument(
|
|
"--expert-data-task",
|
|
type=str,
|
|
default="hopper-expert-v2",
|
|
help="The name of the OpenAI Gym environment to use for expert data collection.",
|
|
)
|
|
parser.add_argument(
|
|
"--buffer-size",
|
|
type=int,
|
|
default=1000000,
|
|
help="The size of the replay buffer.",
|
|
)
|
|
parser.add_argument(
|
|
"--hidden-sizes",
|
|
type=int,
|
|
nargs="*",
|
|
default=[256, 256],
|
|
help="The list of hidden sizes for the neural networks.",
|
|
)
|
|
parser.add_argument(
|
|
"--actor-lr",
|
|
type=float,
|
|
default=1e-4,
|
|
help="The learning rate for the actor network.",
|
|
)
|
|
parser.add_argument(
|
|
"--critic-lr",
|
|
type=float,
|
|
default=3e-4,
|
|
help="The learning rate for the critic network.",
|
|
)
|
|
parser.add_argument(
|
|
"--alpha",
|
|
type=float,
|
|
default=0.2,
|
|
help="The weight of the entropy term in the loss function.",
|
|
)
|
|
parser.add_argument(
|
|
"--auto-alpha",
|
|
default=True,
|
|
action="store_true",
|
|
help="Whether to use automatic entropy tuning.",
|
|
)
|
|
parser.add_argument(
|
|
"--alpha-lr",
|
|
type=float,
|
|
default=1e-4,
|
|
help="The learning rate for the entropy tuning.",
|
|
)
|
|
parser.add_argument(
|
|
"--cql-alpha-lr",
|
|
type=float,
|
|
default=3e-4,
|
|
help="The learning rate for the CQL entropy tuning.",
|
|
)
|
|
parser.add_argument(
|
|
"--start-timesteps",
|
|
type=int,
|
|
default=10000,
|
|
help="The number of timesteps before starting to train.",
|
|
)
|
|
parser.add_argument(
|
|
"--epoch",
|
|
type=int,
|
|
default=200,
|
|
help="The number of epochs to train for.",
|
|
)
|
|
parser.add_argument(
|
|
"--step-per-epoch",
|
|
type=int,
|
|
default=5000,
|
|
help="The number of steps per epoch.",
|
|
)
|
|
parser.add_argument(
|
|
"--n-step",
|
|
type=int,
|
|
default=3,
|
|
help="The number of steps to use for N-step TD learning.",
|
|
)
|
|
parser.add_argument(
|
|
"--batch-size",
|
|
type=int,
|
|
default=256,
|
|
help="The batch size for training.",
|
|
)
|
|
parser.add_argument(
|
|
"--tau",
|
|
type=float,
|
|
default=0.005,
|
|
help="The soft target update coefficient.",
|
|
)
|
|
parser.add_argument(
|
|
"--temperature",
|
|
type=float,
|
|
default=1.0,
|
|
help="The temperature for the Boltzmann policy.",
|
|
)
|
|
parser.add_argument(
|
|
"--cql-weight",
|
|
type=float,
|
|
default=1.0,
|
|
help="The weight of the CQL loss term.",
|
|
)
|
|
parser.add_argument(
|
|
"--with-lagrange",
|
|
type=bool,
|
|
default=True,
|
|
help="Whether to use the Lagrange multiplier for CQL.",
|
|
)
|
|
parser.add_argument(
|
|
"--calibrated",
|
|
type=bool,
|
|
default=True,
|
|
help="Whether to use calibration for CQL.",
|
|
)
|
|
parser.add_argument(
|
|
"--lagrange-threshold",
|
|
type=float,
|
|
default=10.0,
|
|
help="The Lagrange multiplier threshold for CQL.",
|
|
)
|
|
parser.add_argument("--gamma", type=float, default=0.99, help="The discount factor")
|
|
parser.add_argument(
|
|
"--eval-freq",
|
|
type=int,
|
|
default=1,
|
|
help="The frequency of evaluation.",
|
|
)
|
|
parser.add_argument(
|
|
"--test-num",
|
|
type=int,
|
|
default=10,
|
|
help="The number of episodes to evaluate for.",
|
|
)
|
|
parser.add_argument(
|
|
"--logdir",
|
|
type=str,
|
|
default="log",
|
|
help="The directory to save logs to.",
|
|
)
|
|
parser.add_argument(
|
|
"--render",
|
|
type=float,
|
|
default=1 / 35,
|
|
help="The frequency of rendering the environment.",
|
|
)
|
|
parser.add_argument(
|
|
"--device",
|
|
type=str,
|
|
default="cuda" if torch.cuda.is_available() else "cpu",
|
|
help="The device to train on (cpu or cuda).",
|
|
)
|
|
parser.add_argument(
|
|
"--resume-path",
|
|
type=str,
|
|
default=None,
|
|
help="The path to the checkpoint to resume from.",
|
|
)
|
|
parser.add_argument(
|
|
"--resume-id",
|
|
type=str,
|
|
default=None,
|
|
help="The ID of the checkpoint to resume from.",
|
|
)
|
|
parser.add_argument(
|
|
"--logger",
|
|
type=str,
|
|
default="tensorboard",
|
|
choices=["tensorboard", "wandb"],
|
|
)
|
|
parser.add_argument("--wandb-project", type=str, default="offline_d4rl.benchmark")
|
|
parser.add_argument(
|
|
"--watch",
|
|
default=False,
|
|
action="store_true",
|
|
help="watch the play of pre-trained policy only",
|
|
)
|
|
return parser.parse_args()
|
|
|
|
|
|
def test_cql():
|
|
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
|
|
# actor network
|
|
net_a = Net(
|
|
args.state_shape,
|
|
args.action_shape,
|
|
hidden_sizes=args.hidden_sizes,
|
|
device=args.device,
|
|
)
|
|
actor = ActorProb(
|
|
net_a,
|
|
action_shape=args.action_shape,
|
|
device=args.device,
|
|
unbounded=True,
|
|
conditioned_sigma=True,
|
|
).to(args.device)
|
|
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
|
|
|
# critic network
|
|
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,
|
|
)
|
|
critic = Critic(net_c1, device=args.device).to(args.device)
|
|
critic_optim = torch.optim.Adam(critic.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)
|
|
|
|
if args.auto_alpha:
|
|
target_entropy = -np.prod(env.action_space.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 = CQLPolicy(
|
|
actor=actor,
|
|
actor_optim=actor_optim,
|
|
critic=critic,
|
|
critic_optim=critic_optim,
|
|
action_space=env.action_space,
|
|
critic2=critic2,
|
|
critic2_optim=critic2_optim,
|
|
calibrated=args.calibrated,
|
|
cql_alpha_lr=args.cql_alpha_lr,
|
|
cql_weight=args.cql_weight,
|
|
tau=args.tau,
|
|
gamma=args.gamma,
|
|
alpha=args.alpha,
|
|
temperature=args.temperature,
|
|
with_lagrange=args.with_lagrange,
|
|
lagrange_threshold=args.lagrange_threshold,
|
|
min_action=np.min(env.action_space.low),
|
|
max_action=np.max(env.action_space.high),
|
|
device=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)
|
|
|
|
# collector
|
|
test_collector = Collector(policy, test_envs)
|
|
|
|
# log
|
|
now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
|
|
args.algo_name = "cql"
|
|
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:
|
|
replay_buffer = load_buffer_d4rl(args.expert_data_task)
|
|
# trainer
|
|
result = OfflineTrainer(
|
|
policy=policy,
|
|
buffer=replay_buffer,
|
|
test_collector=test_collector,
|
|
max_epoch=args.epoch,
|
|
step_per_epoch=args.step_per_epoch,
|
|
episode_per_test=args.test_num,
|
|
batch_size=args.batch_size,
|
|
save_best_fn=save_best_fn,
|
|
logger=logger,
|
|
).run()
|
|
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()}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_cql()
|