3 papers
cs.LG2025
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
Samin Yeasar Arnob, Scott Fujimoto, Doina Precup
In this paper, we investigate the use of small datasets in the context of offline reinforcement learning (RL). While many common offline RL benchmarks employ datasets with over a m…
cs.LG2024
Fairness in Reinforcement Learning with Bisimulation Metrics
Sahand Rezaei-Shoshtari, Hanna Yurchyk, Scott Fujimoto +2
Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environments. By maximizing their reward without co…
cs.RO2024
Imitation Learning from Observation through Optimal Transport
Wei-Di Chang, Scott Fujimoto, David Meger +1
Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert, using only observational data and without the direct guidance…