9 papers
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…
Accelerating Goal-Conditioned RL Algorithms and Research
MichaÅ Bortkiewicz, WÅadysÅaw PaÅucki, Vivek Myers +4
Self-supervision has the potential to transform reinforcement learning (RL), paralleling the breakthroughs it has enabled in other areas of machine learning. While self-supervised…
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…
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…
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…