159 citations · 213 across the 8 of their papers we have counts for
11 papers · 1 filter
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…
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…
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Scott Fujimoto, Wei-Di Chang, Edward J. Smith +3
In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states,…
IL-flOw: Imitation Learning from Observation using Normalizing Flows
Wei-Di Chang, Juan Camilo Gamboa Higuera, Scott Fujimoto +2
We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only. Our approach decouples reward modelling from policy learning, unlike state-of-…
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
Scott Fujimoto, David Meger, Doina Precup +2
In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy. While the Bellman equation is uniquely solved by the true value funct…
A Minimalist Approach to Offline Reinforcement Learning
Scott Fujimoto, Shixiang Shane Gu
Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data. Due to errors in value estimation from out-of-distribution actions, most offline RL alg…