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train_PrefPPO.py
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import gym
import argparse
import yaml
import os
from collections import OrderedDict
from stable_baselines3 import PPO_REWARD
from stable_baselines3.ppo import MlpPolicy
from stable_baselines3.common.env_util import make_vec_dmcontrol_env, make_vec_metaworld_env
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from stable_baselines3.common.vec_env import VecNormalize
from reward_model import RewardModel
def linear_schedule(initial_value: Union[float, str]) -> Callable[[float], float]:
"""
Linear learning rate schedule.
:param initial_value: (float or str)
:return: (function)
"""
if isinstance(initial_value, str):
initial_value = float(initial_value)
def func(progress_remaining: float) -> float:
"""
Progress will decrease from 1 (beginning) to 0
:param progress_remaining: (float)
:return: (float)
"""
return progress_remaining * initial_value
return func
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--env", type=str, default="walker_walk", help="environment ID")
parser.add_argument("-tb", "--tensorboard-log", help="Tensorboard log dir", default="logs/PrefPPO/", type=str)
parser.add_argument("--seed", help="Random generator seed", type=int, default=123)
parser.add_argument("--n-envs", help="# of parallel environments", type=int, default=16)
parser.add_argument("--n-steps", help="# of steps to run for each environment per update", type=int, default=500)
parser.add_argument("--lr", help="learning rate", type=float, default=3e-4)
parser.add_argument("--total-timesteps", help="total timesteps", type=int, default=1000000)
parser.add_argument("-b", "--batch-size", help="batch size", type=int, default=64)
parser.add_argument("--ent-coef", help="coeff for entropy", type=float, default=0.0)
parser.add_argument("--hidden-dim", help="dim of hidden features", type=int, default=1024)
parser.add_argument("--num-layer", help="# of layers", type=int, default=2)
parser.add_argument("--use-sde", help="Whether to use generalized State Dependent Exploration", type=int, default=1)
parser.add_argument("--sde-freq", help="Sample a new noise matrix every n steps", type=int, default=4)
parser.add_argument("--gae-lambda", help="Factor for trade-off of bias vs variance", type=float, default=0.92)
parser.add_argument("--clip-init", help="Initial value of clipping", type=float, default=0.4)
parser.add_argument("--n-epochs", help="Number of epoch when optimizing the surrogate loss", type=int, default=20)
parser.add_argument("--normalize", help="Normalization", type=int, default=1)
parser.add_argument("--unsuper-step", help="# of steps for unsupervised learning", type=int, default=32000)
parser.add_argument("--unsuper-n-epochs", help="# of steps for unsupervised learning", type=int, default=50)
# reward learning
parser.add_argument("--re-lr", help="Learning rate of reward fn", type=float, default=0.0003)
parser.add_argument("--re-segment", help="Size of segment", type=int, default=50)
parser.add_argument("--re-act", help="Last activation for reward fn", type=str, default='tanh')
parser.add_argument("--re-num-interaction", help="# of interactions", type=int, default=16000)
parser.add_argument("--re-num-feed", help="# of feedbacks", type=int, default=1)
parser.add_argument("--re-batch", help="Batch size", type=int, default=128)
parser.add_argument("--re-update", help="Gradient update of reward fn", type=int, default=100)
parser.add_argument("--re-feed-type", help="type of feedback", type=int, default=0)
parser.add_argument("--re-large-batch", help="size of buffer for ensemble uncertainty", type=int, default=10)
parser.add_argument("--re-max-feed", help="# of total feedback", type=int, default=1400)
parser.add_argument("--teacher-beta", type=float, default=-1)
parser.add_argument("--teacher-gamma", type=float, default=1.0)
parser.add_argument("--teacher-eps-mistake", type=float, default=0.0)
parser.add_argument("--teacher-eps-skip", type=float, default=0.0)
parser.add_argument("--teacher-eps-equal", type=float, default=0.0)
args = parser.parse_args()
metaworld_flag = False
max_ep_len = 1000
if 'metaworld' in args.env:
metaworld_flag = True
max_ep_len = 500
env_name = args.env
if args.normalize == 1:
args.tensorboard_log += 'normalized_' + env_name
else:
args.tensorboard_log += env_name
args.tensorboard_log += '/teacher_' + str(args.teacher_beta)
args.tensorboard_log += '_' + str(args.teacher_gamma)
args.tensorboard_log += '_' + str(args.teacher_eps_mistake)
args.tensorboard_log += '_' + str(args.teacher_eps_skip)
args.tensorboard_log += '_' + str(args.teacher_eps_equal)
args.tensorboard_log += '/lr_' + str(args.lr)
args.tensorboard_log += '_reward_lr' + str(args.re_lr)
args.tensorboard_log += '_seg' + str(args.re_segment)
args.tensorboard_log += '_act' + str(args.re_act)
args.tensorboard_log += '_inter' + str(args.re_num_interaction)
args.tensorboard_log += '_type' + str(args.re_feed_type)
args.tensorboard_log += '_large' + str(args.re_large_batch)
args.tensorboard_log += '_rebatch' + str(args.re_batch)
args.tensorboard_log += '_reupdate' + str(args.re_update)
args.tensorboard_log += '_batch_' + str(args.batch_size)
args.tensorboard_log += '_nenvs_' + str(args.n_envs)
args.tensorboard_log += '_nsteps_' + str(args.n_steps)
args.tensorboard_log += '_ent_' + str(args.ent_coef)
args.tensorboard_log += '_hidden_' + str(args.hidden_dim)
args.tensorboard_log += '_sde_' + str(args.use_sde)
args.tensorboard_log += '_sdefreq_' + str(args.sde_freq)
args.tensorboard_log += '_gae_' + str(args.gae_lambda)
args.tensorboard_log += '_clip_' + str(args.clip_init)
args.tensorboard_log += '_nepochs_' + str(args.n_epochs)
args.tensorboard_log += '_maxfeed_' + str(args.re_max_feed)
args.tensorboard_log += '_unsuper_' + str(args.unsuper_step)
args.tensorboard_log += '_update_' + str(args.unsuper_n_epochs)
args.tensorboard_log += '_seed_' + str(args.seed)
# extra params
if args.use_sde == 0:
use_sde = False
else:
use_sde = True
clip_range = linear_schedule(args.clip_init)
# Parallel environments
if metaworld_flag:
env = make_vec_metaworld_env(args.env, n_envs=args.n_envs, monitor_dir=args.tensorboard_log, seed=args.seed)
else:
env = make_vec_dmcontrol_env(args.env, n_envs=args.n_envs, monitor_dir=args.tensorboard_log, seed=args.seed)
# instantiating the reward model
reward_model = RewardModel(
env.envs[0].observation_space.shape[0],
env.envs[0].action_space.shape[0],
size_segment=args.re_segment,
activation=args.re_act,
lr=args.re_lr,
mb_size=args.re_batch,
teacher_beta=args.teacher_beta,
teacher_gamma=args.teacher_gamma,
teacher_eps_mistake=args.teacher_eps_mistake,
teacher_eps_skip=args.teacher_eps_skip,
teacher_eps_equal=args.teacher_eps_equal,
large_batch=args.re_large_batch
)
if args.normalize == 1:
env = VecNormalize(env, norm_reward=False)
# network arch
net_arch = [dict(pi=[args.hidden_dim] * args.num_layer, vf=[args.hidden_dim] * args.num_layer)]
policy_kwargs = dict(net_arch=net_arch)
# train model
model = PPO_REWARD(
reward_model,
MlpPolicy, env,
tensorboard_log=args.tensorboard_log,
seed=args.seed,
learning_rate=args.lr,
batch_size=args.batch_size,
n_steps=args.n_steps,
ent_coef=args.ent_coef,
policy_kwargs=policy_kwargs,
use_sde=use_sde,
sde_sample_freq=args.sde_freq,
gae_lambda=args.gae_lambda,
clip_range=clip_range,
n_epochs=args.n_epochs,
num_interaction=args.re_num_interaction,
num_feed=args.re_num_feed,
feed_type=args.re_feed_type,
re_update=args.re_update,
metaworld_flag=metaworld_flag,
max_feed=args.re_max_feed,
unsuper_step=args.unsuper_step,
unsuper_n_epochs=args.unsuper_n_epochs,
size_segment=args.re_segment,
max_ep_len=max_ep_len,
verbose=1
)
# save args
with open(os.path.join(args.tensorboard_log, "args.yml"), "w") as f:
ordered_args = OrderedDict([(key, vars(args)[key]) for key in sorted(vars(args).keys())])
yaml.dump(ordered_args, f)
model.learn(total_timesteps=args.total_timesteps, unsuper_flag=1)
model.reward_model.save(args.tensorboard_log, args.total_timesteps)