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Found 4 papers, 1 papers with code
Efficient Online Learning with Offline Datasets for Infinite Horizon MDPs: A Bayesian Approach
In this paper, we study the problem of efficient online reinforcement learning in the infinite horizon setting when there is an offline dataset to start with.
Regret Analysis of the Posterior Sampling-based Learning Algorithm for Episodic POMDPs
We propose a Posterior Sampling-based reinforcement learning algorithm for POMDPs (PS4POMDPs), which is much simpler and more implementable compared to state-of-the-art optimism-based online learning algorithms for POMDPs.
A Novel Point-based Algorithm for Multi-agent Control Using the Common Information Approach
The Common Information (CI) approach provides a systematic way to transform a multi-agent stochastic control problem to a single-agent partially observed Markov decision problem (POMDP) called the coordinator's POMDP.
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale
We first propose an Informed Posterior Sampling-based RL (iPSRL) algorithm that uses the offline dataset, and information about the expert's behavioral policy used to generate the offline dataset.
