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train_trpol.py
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train_trpol.py
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import os
import pprint
from dataclasses import asdict
import bullet_safety_gym
try:
import safety_gymnasium
except ImportError:
print("safety_gymnasium is not found.")
import gymnasium as gym
import pyrallis
import torch
from tianshou.data import VectorReplayBuffer
from tianshou.env import BaseVectorEnv, ShmemVectorEnv, SubprocVectorEnv
from tianshou.utils.net.common import Net
from tianshou.utils.net.continuous import ActorProb, Critic
from torch.distributions import Independent, Normal
from fsrl.config.trpol_cfg import (
Bullet1MCfg,
Bullet5MCfg,
Bullet10MCfg,
Mujoco2MCfg,
Mujoco10MCfg,
Mujoco20MCfg,
MujocoBaseCfg,
TrainCfg,
)
from fsrl.data import FastCollector
from fsrl.policy import TRPOLagrangian
from fsrl.trainer import OnpolicyTrainer
from fsrl.utils import TensorboardLogger, WandbLogger
from fsrl.utils.exp_util import auto_name, seed_all
from fsrl.utils.net.common import ActorCritic
TASK_TO_CFG = {
# bullet safety gym tasks
"SafetyCarRun-v0": Bullet1MCfg,
"SafetyBallRun-v0": Bullet1MCfg,
"SafetyBallCircle-v0": Bullet1MCfg,
"SafetyCarCircle-v0": TrainCfg,
"SafetyDroneRun-v0": TrainCfg,
"SafetyAntRun-v0": TrainCfg,
"SafetyDroneCircle-v0": Bullet5MCfg,
"SafetyAntCircle-v0": Bullet10MCfg,
# safety gymnasium tasks
"SafetyPointCircle1Gymnasium-v0": Mujoco2MCfg,
"SafetyPointCircle2Gymnasium-v0": Mujoco2MCfg,
"SafetyCarCircle1Gymnasium-v0": Mujoco2MCfg,
"SafetyCarCircle2Gymnasium-v0": Mujoco2MCfg,
"SafetyPointGoal1Gymnasium-v0": MujocoBaseCfg,
"SafetyPointGoal2Gymnasium-v0": MujocoBaseCfg,
"SafetyPointButton1Gymnasium-v0": MujocoBaseCfg,
"SafetyPointButton2Gymnasium-v0": MujocoBaseCfg,
"SafetyPointPush1Gymnasium-v0": MujocoBaseCfg,
"SafetyPointPush2Gymnasium-v0": MujocoBaseCfg,
"SafetyCarGoal1Gymnasium-v0": MujocoBaseCfg,
"SafetyCarGoal2Gymnasium-v0": MujocoBaseCfg,
"SafetyCarButton1Gymnasium-v0": MujocoBaseCfg,
"SafetyCarButton2Gymnasium-v0": MujocoBaseCfg,
"SafetyCarPush1Gymnasium-v0": MujocoBaseCfg,
"SafetyCarPush2Gymnasium-v0": MujocoBaseCfg,
"SafetyHalfCheetahVelocityGymnasium-v1": MujocoBaseCfg,
"SafetyHopperVelocityGymnasium-v1": MujocoBaseCfg,
"SafetySwimmerVelocityGymnasium-v1": MujocoBaseCfg,
"SafetyWalker2dVelocityGymnasium-v1": Mujoco10MCfg,
"SafetyAntVelocityGymnasium-v1": Mujoco10MCfg,
"SafetyHumanoidVelocityGymnasium-v1": Mujoco20MCfg,
}
@pyrallis.wrap()
def train(args: TrainCfg):
# set seed and computing
seed_all(args.seed)
torch.set_num_threads(args.thread)
task = args.task
default_cfg = TASK_TO_CFG[task]() if task in TASK_TO_CFG else TrainCfg()
# use the default configs instead of the input args.
if args.use_default_cfg:
default_cfg.task = args.task
default_cfg.seed = args.seed
default_cfg.device = args.device
default_cfg.logdir = args.logdir
default_cfg.project = args.project
default_cfg.group = args.group
default_cfg.suffix = args.suffix
args = default_cfg
# setup logger
cfg = asdict(args)
default_cfg = asdict(default_cfg)
if args.name is None:
args.name = auto_name(default_cfg, cfg, args.prefix, args.suffix)
if args.group is None:
args.group = args.task + "-cost-" + str(int(args.cost_limit))
if args.logdir is not None:
args.logdir = os.path.join(args.logdir, args.project, args.group)
logger = WandbLogger(cfg, args.project, args.group, args.name, args.logdir)
# logger = TensorboardLogger(args.logdir, log_txt=True, name=args.name)
logger.save_config(cfg, verbose=args.verbose)
# model
env = gym.make(args.task)
state_shape = env.observation_space.shape or env.observation_space.n
action_shape = env.action_space.shape or env.action_space.n
max_action = env.action_space.high[0]
net = Net(state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
actor = ActorProb(
net,
action_shape,
max_action=max_action,
unbounded=args.unbounded,
device=args.device
).to(args.device)
critic = [
Critic(
Net(state_shape, hidden_sizes=args.hidden_sizes, device=args.device),
device=args.device
).to(args.device) for _ in range(2)
]
torch.nn.init.constant_(actor.sigma_param, -0.5)
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)
if args.last_layer_scale:
# do last policy layer scaling, this will make initial actions have (close to)
# 0 mean and std, and will help boost performances,
# see https://arxiv.org/abs/2006.05990, Fig.24 for details
for m in actor.mu.modules():
if isinstance(m, torch.nn.Linear):
torch.nn.init.zeros_(m.bias)
m.weight.data.copy_(0.01 * m.weight.data)
optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
# replace DiagGuassian with Independent(Normal) which is equivalent
# pass *logits to be consistent with policy.forward
def dist(*logits):
return Independent(Normal(*logits), 1)
policy = TRPOLagrangian(
actor,
critic,
optim,
dist,
logger=logger,
target_kl=args.target_kl,
backtrack_coeff=args.backtrack_coeff,
max_backtracks=args.max_backtracks,
optim_critic_iters=args.optim_critic_iters,
gae_lambda=args.gae_lambda,
advantage_normalization=args.norm_adv,
use_lagrangian=args.use_lagrangian,
lagrangian_pid=args.lagrangian_pid,
cost_limit=args.cost_limit,
rescaling=args.rescaling,
gamma=args.gamma,
max_batchsize=args.max_batchsize,
reward_normalization=args.rew_norm,
deterministic_eval=args.deterministic_eval,
action_scaling=args.action_scaling,
action_bound_method=args.action_bound_method,
observation_space=env.observation_space,
action_space=env.action_space,
lr_scheduler=None
)
training_num = min(args.training_num, args.episode_per_collect)
worker = eval(args.worker)
train_envs = worker([lambda: gym.make(args.task) for _ in range(training_num)])
test_envs = worker([lambda: gym.make(args.task) for _ in range(args.testing_num)])
# collector
train_collector = FastCollector(
policy,
train_envs,
VectorReplayBuffer(args.buffer_size, len(train_envs)),
exploration_noise=True,
)
test_collector = FastCollector(policy, test_envs)
def stop_fn(reward, cost):
return reward > args.reward_threshold and cost < args.cost_limit
def checkpoint_fn():
return {"model": policy.state_dict()}
if args.save_ckpt:
logger.setup_checkpoint_fn(checkpoint_fn)
# trainer
trainer = OnpolicyTrainer(
policy=policy,
train_collector=train_collector,
test_collector=test_collector,
max_epoch=args.epoch,
batch_size=args.batch_size,
cost_limit=args.cost_limit,
step_per_epoch=args.step_per_epoch,
repeat_per_collect=args.repeat_per_collect,
episode_per_test=args.testing_num,
episode_per_collect=args.episode_per_collect,
stop_fn=stop_fn,
logger=logger,
resume_from_log=args.resume,
save_model_interval=args.save_interval,
verbose=args.verbose,
)
for epoch, epoch_stat, info in trainer:
logger.store(tab="train", cost_limit=args.cost_limit)
print(f"Epoch: {epoch}")
print(info)
if __name__ == "__main__":
pprint.pprint(info)
# Let's watch its performance!
env = gym.make(args.task)
policy.eval()
collector = FastCollector(policy, env)
result = collector.collect(n_episode=10, render=args.render)
rews, lens, cost = result["rew"], result["len"], result["cost"]
print(f"Final eval reward: {rews.mean()}, cost: {cost}, length: {lens.mean()}")
policy.train()
collector = FastCollector(policy, env)
result = collector.collect(n_episode=10, render=args.render)
rews, lens, cost = result["rew"], result["len"], result["cost"]
print(f"Final train reward: {rews.mean()}, cost: {cost}, length: {lens.mean()}")
if __name__ == "__main__":
train()