Full Bayesian Reinforcement Learning via LF-IBIS
Researchers introduce LF-IBIS, a new algorithm for Bayesian reinforcement learning that operates without requiring an explicit likelihood function. By integrating Approximate Bayesian Computation with Iterated Batch Importance Sampling, LF-IBIS enables full Bayesian inference in environments with intractable or unavailable likelihoods. The method produces approximate posterior distributions over both environment parameters and optimal policies, supporting uncertainty quantification for exploration-exploitation trade-offs. Validation is demonstrated through simulation studies, including response-adaptive randomization in clinical trials.
Why it matters: This approach broadens the applicability of Bayesian reinforcement learning to scenarios where likelihood functions are inaccessible, enabling principled uncertainty quantification in more realistic and complex environments.
Full story at: arXiv Statistical ML ↗