Don’t Be Pessimistic Too Early: Look K Steps Ahead!
Chaoqi Wang, Ziyu Ye, Kevin Murphy, Yuxin Chen
Abstract
Offline reinforcement learning (RL) considers to train highly rewarding policies exclusively from existing data, showing great real-world impacts. Pessimism, \emph{i.e.}, avoiding uncertain states or actions during decision making, has long been the main theme for offline RL. However, existing works often lead to overly conservative policies with rather sub-optimal performance. To tackle this challenge, we introduce the notion of \emph{lookahead pessimism} within the model-based offline RL paradigm. Intuitively, while the classical pessimism principle asks to terminate whenever the RL agent reaches an uncertain region, our method allows the agent to use a lookahead set carefully crafted from the learned model, and to make a move by properties of the lookahead set. Remarkably, we show that this enables learning a less conservative policy with a better performance guarantee. We refer to our method as Lookahead Pessimistic MDP (LP-MDP). Theoretically, we provide a rigorous analysis on the performance lower bound, which monotonically improves with the lookahead steps. Empirically, with the easy-to-implement design of LP-MDP, we demonstrate a solid performance improvement over baseline methods on widely used offline RL benchmarks.
BibTeX
@InProceedings{pmlr-v238-wang24h,
title = {Don’t Be Pessimistic Too Early: Look {K} Steps Ahead!},
author = {Wang, Chaoqi and Ye, Ziyu and Murphy, Kevin and Chen, Yuxin},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {3313--3321},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v238/wang24h/wang24h.pdf},
url = {https://proceedings.mlr.press/v238/wang24h.html},
abstract = {Offline reinforcement learning (RL) considers to train highly rewarding policies exclusively from existing data, showing great real-world impacts. Pessimism, \emph{i.e.}, avoiding uncertain states or actions during decision making, has long been the main theme for offline RL. However, existing works often lead to overly conservative policies with rather sub-optimal performance. To tackle this challenge, we introduce the notion of \emph{lookahead pessimism} within the model-based offline RL paradigm. Intuitively, while the classical pessimism principle asks to terminate whenever the RL agent reaches an uncertain region, our method allows the agent to use a lookahead set carefully crafted from the learned model, and to make a move by properties of the lookahead set. Remarkably, we show that this enables learning a less conservative policy with a better performance guarantee. We refer to our method as Lookahead Pessimistic MDP (LP-MDP). Theoretically, we provide a rigorous analysis on the performance lower bound, which monotonically improves with the lookahead steps. Empirically, with the easy-to-implement design of LP-MDP, we demonstrate a solid performance improvement over baseline methods on widely used offline RL benchmarks.}
}