PAC-Bayesian Model Selection for Reinforcement Learning

2010-12-01 · NeurIPS 2010 12 · Mahdi M. Fard, Joelle Pineau

This paper introduces the first set of PAC-Bayesian bounds for the batch reinforcement learning problem in finite state spaces. These bounds hold regardless of the correctness of the prior distribution. We demonstrate how such bounds can be used for model-selection in control problems where prior information is available either on the dynamics of the environment, or on the value of actions. Our empirical results confirm that PAC-Bayesian model-selection is able to leverage prior distributions when they are informative and, unlike standard Bayesian RL approaches, ignores them when they are misleading.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

modelModel Selectionreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Robust Bayesian optimization with reinforcement learned acquisition functions

2022-10-02 · Zijing Liu, Xiyao Qu, Xuejun Liu, Hongqiang Lyu

In Bayesian optimization (BO) for expensive black-box optimization tasks, acquisition function (AF) guides sequential sampling and plays a pivotal role for efficient convergence to better optima. Prevailing AFs usually r…

Bayesian Optimization reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

BOTS: A Unified Framework for Bayesian Online Task Selection in LLM Reinforcement Finetuning

2025-10-30 · Qianli Shen, Daoyuan Chen, Yilun Huang, Zhenqing Ling 외 arxiv

Reinforcement finetuning (RFT) is a key technique for aligning Large Language Models (LLMs) with human preferences and enhancing reasoning, yet its effectiveness is highly sensitive to which tasks are explored during tra…

Bayesian Inference

PAC-Bayesian Policy Evaluation for Reinforcement Learning

2012-02-14 · Mahdi Milani Fard, Joelle Pineau, Csaba Szepesvari

Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, largely depends on accuracy and correctness o…

Model Selection reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics

2026-03-26 · João Castelo-Branco, José Santos-Victor, Alexandre Bernardino arxiv

Autonomous object search is challenging for mobile robots operating in indoor environments due to partial observability, perceptual uncertainty, and the need to trade off exploration and navigation efficiency. Classical …

Reinforcement Learning Bayesian Inference

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

2010-12-12 · Eric Brochu, Vlad M. Cora, Nando de Freitas

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combini…

Bayesian Optimization Hierarchical Reinforcement Learning Hyperparameter Optimization reinforcement-learning+2