1 citations · 1 across the 6 of their papers we have counts for
9 papers
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…
Scaling Empowered Agents
Catherine Ji, Vivek Myers, Joshua Lin +3
Empowerment measures the capacity of an agent to actively control its environment. In this work, we explain the connection between skill learning and empowerment. While skill learn…
Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning
Emmy Blumenthal, Nikolas Claussen, Benjamin Eysenbach +4
The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data. These advances have brought to the foreground fundamental co…
Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards
Faisal Mohamed, Catherine Ji, Benjamin Eysenbach +1
Effective exploration in reinforcement learning requires not only tracking where an agent has been, but also understanding how the agent perceives and represents the world. To lear…
Low-N Protein Activity Optimization with FolDE
Jacob B. Roberts, Catherine R. Ji, Isaac Donnell +13
Proteins are traditionally optimized through the costly construction and measurement of many mutants. Active Learning-assisted Directed Evolution (ALDE) alleviates that cost by pre…
BuilderBench: The Building Blocks of Intelligent Agents
Raj Ghugare, Roger Creus Castanyer, Catherine Ji +4
Today's AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set by existing data. To solve novel…
Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration
Chirayu Nimonkar, Shlok Shah, Catherine Ji +1
For groups of autonomous agents to achieve a particular goal, they must engage in coordination and long-horizon reasoning. Rather than relying on complex reward functions and expli…
Identifying nonequilibrium degrees of freedom in high-dimensional stochastic systems
Catherine Ji, Ravin Raj, Benjamin Eysenbach +1
Any coarse-grained description of a nonequilibrium system should faithfully represent its latent irreversible degrees of freedom. However, standard dimensionality reduction methods…
Horizon Generalization in Reinforcement Learning
Vivek Myers, Catherine Ji, Benjamin Eysenbach
We study goal-conditioned RL through the lens of generalization, but not in the traditional sense of random augmentations and domain randomization. Rather, we aim to learn goal-dir…