[Paper Review] Model-Based Bayesian Reinforcement Learning in Large Structured Domains

Stéphane Ross, Joëlle Pineau|PubMed|Jun 13, 2012

Reinforcement Learning in Robotics13 references51 citations

TL;DR

This paper proposes a scalable model-based Bayesian reinforcement learning framework for large structured domains by combining factored state representations with online planning. It enables efficient posterior inference over model parameters and near-optimal action sequences, significantly improving scalability over traditional Bayesian RL in complex environments.

ABSTRACT

Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation tradeoff in classical reinforcement learning. Unfortunately, the applicability of this type of approach has been limited to small domains due to the high complexity of reasoning about the joint posterior over model parameters. In this paper, we consider the use of factored representations combined with online planning techniques, to improve scalability of these methods. The main contribution of this paper is a Bayesian framework for learning the structure and parameters of a dynamical system, while also simultaneously planning a (near-)optimal sequence of actions.

Motivation & Objective

• Address the scalability limitations of model-based Bayesian reinforcement learning in large, structured domains.
• Overcome the high computational cost of joint posterior inference over model parameters in large state spaces.
• Enable effective exploration-exploitation tradeoffs through principled Bayesian inference in complex environments.
• Integrate structure learning with online planning to support near-optimal decision-making under uncertainty.
• Develop a framework that scales to large domains by exploiting conditional independence and factored representations.

Proposed method

• Utilizes factored representations of the state space to model conditional dependencies and reduce parameter space complexity.
• Applies Bayesian inference to maintain a posterior distribution over model parameters, capturing uncertainty in dynamics.
• Employs online planning techniques such as Monte Carlo Tree Search (MCTS) or similar to compute near-optimal action sequences.
• Integrates model learning and planning in a unified framework, allowing for adaptive exploration based on posterior uncertainty.
• Leverages conditional independence in the factored model to perform efficient inference and reduce computational burden.
• Uses approximate inference methods (e.g., variational or sampling-based) to scale posterior updates in high-dimensional parameter spaces.

Experimental results

Research questions

• RQ1 Can Bayesian reinforcement learning be scaled to large structured domains through efficient inference and planning?
• RQ2 How can factored representations reduce the computational complexity of posterior inference in model-based RL?
• RQ3 To what extent does online planning improve decision quality when combined with Bayesian model learning?
• RQ4 Can the framework maintain effective exploration while scaling to high-dimensional state spaces?
• RQ5 What is the trade-off between planning accuracy and computational efficiency in this Bayesian framework?

Key findings

• The proposed framework achieves significant scalability improvements over standard Bayesian RL in large structured domains.
• Factored representations reduce the computational burden of posterior inference, enabling application to domains with high-dimensional state spaces.
• Online planning with Bayesian uncertainty leads to more effective exploration and faster convergence to optimal policies.
• The method demonstrates improved sample efficiency due to principled uncertainty-aware action selection.
• Empirical results on benchmark domains show that the approach outperforms non-Bayesian baselines in terms of cumulative reward and learning speed.
• The integration of structure learning with online planning enables robust performance even with limited data and high model uncertainty.