most citedWhen to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter

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cs.LG2025

Observational Learning with a Budget

Shuo Wu, Pawan Poojary, Randall Berry

We consider a model of Bayesian observational learning in which a sequence of agents receives a private signal about an underlying binary state of the world. Each agent makes a dec…

cs.LG2024★ 1 cited

When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter

Yansong Li, Zeyu Dong, Ertai Luo +3

Reinforcement learning (RL) algorithms can be divided into two classes: model-free algorithms, which are sample-inefficient, and model-based algorithms, which suffer from model bia…