3 papers indexed
Haseeb Shah, Lingwei Zhu, Adam White, Martha White
Reinforcement learning is increasingly being considered for controlling real-world systems, from fusion plasma and autonomous vehicles to drug discovery and drinking water treatment, where reliability is essential and tuning budgets are limited. Actor-critic algorithms share a se…
Prabhat Nagarajan, Brett Daley, Martha White, Marlos C. Machado
Action-values are foundational to many control algorithms such as Q-learning. Therefore learning action-values efficiently is central to reinforcement learning (RL). However, learning them can be slow, requiring many updates to move values from their initialization, typically nea…
Matthew Vandergrift, Esraa Elelimy, Martha White
One goal in reinforcement learning (RL) research is to understand general-purpose sequential decision-making, using benchmark simulators as a proxy for learning in deployment settings. When running experiments, however, the goal of achieving high performance in the simulator can…