7 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning
Hongyu Zang, Xin Li, Leiji Zhang +5
While bisimulation-based approaches hold promise for learning robust state representations for Reinforcement Learning (RL) tasks, their efficacy in offline RL tasks has not been up…
cs.LG2023★ 2 cited
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik +6
Offline policy learning is aimed at learning decision-making policies using existing datasets of trajectories without collecting additional data. The primary motivation for using r…
cs.LG2016★ 7 cited
Separation of Concerns in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff +1
In this paper, we propose a framework for solving a single-agent task by using multiple agents, each focusing on different aspects of the task. This approach has two main advantage…