2 citations · 2 across the 9 of their papers we have counts for
7 papers · 1 filter
Measuring Empowerment from Offline Data for Intrinsically Motivated Exploration
Ishir Garg, Vivek Myers, Qiyang Li +3
Large, unlabeled datasets of prior experience have become increasingly available for agentic tasks in domains such as robotics and coding. A common recipe for these tasks is to tra…
The Geometry of Empowerment
Catherine Ji, Vivek Myers, Sergey Levine +1
Empowerment captures the capacity for an agent to actively control its environment. While conceptually appealing as an information-theoretic quantity, the connection between empowe…
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…
Offline Goal-conditioned Reinforcement Learning with Quasimetric Representations
Vivek Myers, Bill Chunyuan Zheng, Benjamin Eysenbach +1
Approaches for goal-conditioned reinforcement learning (GCRL) often use learned state representations to extract goal-reaching policies. Two frameworks for representation structure…
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
Vivek Myers, Chongyi Zheng, Anca Dragan +2
Temporal distances lie at the heart of many algorithms for planning, control, and reinforcement learning that involve reaching goals, allowing one to estimate the transit time betw…
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov +1
Given time series data, how can we answer questions like "what will happen in the future?" and "how did we get here?" These sorts of probabilistic inference questions are challengi…
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
Chongyi Zheng, Benjamin Eysenbach, Homer Walke +4
Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strat…