Skip to content. | Skip to navigation

Personal tools

Navigation

You are here: Home / Papers / Constrained Bayesian Reinforcement Learning via Approximate Linear Programming

Jongmin Lee, Youngsoo Jang, Pascal Poupart, and Kee-Eung Kim (2017)

Constrained Bayesian Reinforcement Learning via Approximate Linear Programming

In: ECML-PKDD Workshop on Scaling-Up Reinforcement Learning (SURL).

In this paper, we highlight our recent work~\cite{Lee2017} considering the safe learning scenario where we need to restrict the exploratory behavior of a reinforcement learning agent. Specifically, we treat the problem as a form of Bayesian reinforcement learning (BRL) in an environment that is modeled as a constrained MDP (CMDP) where the cost function penalizes undesirable situations. We propose a model-based BRL algorithm for such an environment, eliciting risk-sensitive exploration in a principled way. Our algorithm efficiently solves the constrained BRL problem by approximate linear programming, and generates a finite state controller in an off-line manner. We provide theoretical guarantees and demonstrate empirically that our approach outperforms the state of the art.