Bayesian Neural Networks via Dirichlet Process Priors on the Data-Generating Distribution for Randomized Value Functions in Deep Reinforcement Learning
Read the original on arXiv Machine Learning →The paper introduces a new class of Bayesian neural networks that place Dirichlet Process priors on the data-generating distribution instead of on network weights, aiming to better capture epistemic uncertainty. Using this framework, the authors develop DP-DQN, a reinforcement learning algorithm that maintains posterior distributions over value functions and performs efficient Thompson sampling. DP-DQN combines data-driven posterior reweighting via the Bayesian Bootstrap with generative prior hypotheses from the DP base measure, enabling deep exploration with a single neural network without requiring multi-network ensembles.
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