3 papers
cs.LG2026
When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
Vincent Liu, Prabhat Nagarajan, Andrew Patterson +1
Offline reinforcement learning algorithms often require careful hyperparameter tuning. Before deployment, we need to select amongst a set of candidate policies. However, there is l…
cs.LG2024
Empirical Design in Reinforcement Learning
Andrew Patterson, Samuel Neumann, Martha White +1
Empirical design in reinforcement learning is no small task. Running good experiments requires attention to detail and at times significant computational resources. While compute r…
cs.LG2024
Investigating the Interplay of Prioritized Replay and Generalization
Parham Mohammad Panahi, Andrew Patterson, Martha White +1
Experience replay, the reuse of past data to improve sample efficiency, is ubiquitous in reinforcement learning. Though a variety of smart sampling schemes have been introduced to…