3 citations · 7 across the 4 of their papers we have counts for
5 papers · 1 filter
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View
Raj Ghugare, Matthieu Geist, Glen Berseth +1
Some reinforcement learning (RL) algorithms can stitch pieces of experience to solve a task never seen before during training. This oft-sought property is one of the few ways in wh…
Bridging State and History Representations: Understanding Self-Predictive RL
Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi +4
Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs…
Contrastive Example-Based Control
Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov +4
While many real-world problems that might benefit from reinforcement learning, these problems rarely fit into the MDP mold: interacting with the environment is often expensive and…
A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning
Benjamin Eysenbach, Matthieu Geist, Sergey Levine +1
As with any machine learning problem with limited data, effective offline RL algorithms require careful regularization to avoid overfitting. One-step methods perform regularization…
RvS: What is Essential for Offline RL via Supervised Learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov +1
Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. When does this hold true, and which alg…