94 citations · 796 across the 60 of their papers we have counts for
61 papers
Learning to Perceive the World Through Control: Empowerment-Based Representation Learning
Mahsa Bastankhah, Sophie Broderick, Benjamin Eysenbach
In many practical reinforcement learning environments, observations are far higher-dimensional than the variables that matter for control. In this work, we ask: can we learn repres…
Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan +1
Unsupervised pretraining has driven empirical advances in goal-conditioned reinforcement learning (GCRL), but its theoretical foundations remain poorly understood. In particular, a…
Multistep Quasimetric Learning for Scalable Goal-conditioned Reinforcement Learning
Bill Chunyuan Zheng, Vivek Myers, Benjamin Eysenbach +1
Learning how to reach goals in an environment is a longstanding challenge in AI, yet reasoning over long horizons remains a challenge for modern methods. The key question is how to…
Training LLM Agents to Empower Humans
Evan Ellis, Vivek Myers, Jens Tuyls +3
Assistive agents should not only take actions on behalf of a human, but also step out of the way and cede control when there are important decisions to be made. However, current me…
Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL
Mahsa Bastankhah, Grace Liu, Dilip Arumugam +2
In this work, we take a first step toward elucidating the mechanisms behind emergent exploration in unsupervised reinforcement learning. We study Single-Goal Contrastive Reinforcem…
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
Kevin Wang, Ishaan Javali, Michał Bortkiewicz +2
Scaling up self-supervised learning has driven breakthroughs in language and vision, yet comparable progress has remained elusive in reinforcement learning (RL). In this paper, we…