Making Sense of Reinforcement Learning and Probabilistic Inference

Making Sense of Reinforcement Learning and Probabilistic Inference

3 January 2020

Brendan O'Donoghue

Catalin Ionescu

Papers citing "Making Sense of Reinforcement Learning and Probabilistic Inference"

10 / 10 papers shown

Title
Toward Efficient Exploration by Large Language Model Agents Dilip Arumugam Thomas L. Griffiths LLMAG 92 1 0 29 Apr 2025
Probabilistic Constrained Reinforcement Learning with Formal Interpretability Yanran Wang Qiuchen Qian David E. Boyle 16 4 0 13 Jul 2023
Bayesian Reinforcement Learning with Limited Cognitive Load Dilip Arumugam Mark K. Ho Noah D. Goodman Benjamin Van Roy OffRL 34 8 0 05 May 2023
Conservative objective models are a special kind of contrastive divergence-based energy model Christopher Beckham C. Pal 35 4 0 07 Apr 2023
Model-Based Uncertainty in Value Functions Carlos E. Luis A. Bottero Julia Vinogradska Felix Berkenkamp Jan Peters 36 13 0 24 Feb 2023
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning Dilip Arumugam Mark K. Ho Noah D. Goodman Benjamin Van Roy 28 4 0 30 Oct 2022
Learning General World Models in a Handful of Reward-Free Deployments Yingchen Xu Jack Parker-Holder Aldo Pacchiano Philip J. Ball Oleh Rybkin Stephen J. Roberts Tim Rocktaschel Edward Grefenstette OffRL 55 8 0 23 Oct 2022
On the connection between Bregman divergence and value in regularized Markov decision processes Brendan O'Donoghue OffRL 23 2 0 21 Oct 2022
Towards an Understanding of Default Policies in Multitask Policy Optimization Theodore H. Moskovitz Michael Arbel Jack Parker-Holder Aldo Pacchiano 22 9 0 04 Nov 2021
On the Global Convergence Rates of Softmax Policy Gradient Methods Jincheng Mei Chenjun Xiao Csaba Szepesvári Dale Schuurmans 47 275 0 13 May 2020