Tabular and Deep Reinforcement Learning for Gittins Index

arXiv:2405.01157v1 Announce Type: new
Abstract: In the realm of multi-arm bandit problems, the Gittins index policy is known to be optimal in maximizing the expected total discounted reward obtained from pulling the Markovian arms. In most realistic scenarios however, the Markovian state transition probabilities are unknown and therefore the Gittins indices cannot be computed. One can then resort to reinforcement learning (RL) algorithms that explore the state space to learn these indices while exploiting to maximize the reward collected. In …

abstract arm arxiv cs.lg cs.pf however index policy realm reinforcement reinforcement learning state stat.ml tabular total transition type