State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards
This addresses a practical issue in reinforcement learning for scenarios with time-dependent rewards, offering an incremental improvement over existing methods.
The paper tackles the problem of sequential decision-making in recovering bandits, where rewards depend on time since last pull, by proposing the State-Separate SARSA (SS-SARSA) algorithm, which reduces state combinations and proves asymptotic convergence to an optimal policy with superior performance in simulations.
While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios. This paper considers the setting of recovering bandits (Pike-Burke & Grunewalder, 2019), where the reward depends on the number of rounds elapsed since the last time an arm was pulled. We propose a new reinforcement learning (RL) algorithm tailored to this setting, named the State-Separate SARSA (SS-SARSA) algorithm, which treats rounds as states. The SS-SARSA algorithm achieves efficient learning by reducing the number of state combinations required for Q-learning/SARSA, which often suffers from combinatorial issues for large-scale RL problems. Additionally, it makes minimal assumptions about the reward structure and offers lower computational complexity. Furthermore, we prove asymptotic convergence to an optimal policy under mild assumptions. Simulation studies demonstrate the superior performance of our algorithm across various settings.