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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…
Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL
Michal Korniak, Kamil Dybek, Benjamin Eysenbach +2
While self-supervised approaches to reinforcement learning have achieved strong results by learning representations of states and actions, a key open question is the time scale ove…
LeAct: Learning to Reason from Expert Actions
Ziran Yang, Chengshuai Shi, Raj Ghugare +3
Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs. However, a rich and largely untapped source of supervision l…
Behavior-Consistent Deep Reinforcement Learning
Marcel Hussing, Liv G. d'Aliberti, Claas Voelcker +2
Reinforcement learning (RL) often exhibits high variance across training runs, leading to unreliable performance and posing a major challenge to deployment in real-world domains. I…
Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Aravind Venugopal, Jiayu Chen, Xudong Wu +3
The temporal lag between actions and their long-term consequences makes credit assignment a challenge when learning goal-directed behaviors from data. Generative world models captu…
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…
UpSkill: Mutual Information Skill Learning for Structured Response Diversity in LLMs
Devan Shah, Owen Yang, Daniel Yang +2
Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of large language models (LLMs) on mathematics and programming tasks, but standard approa…
Can We Really Learn One Representation to Optimize All Rewards?
Chongyi Zheng, Royina Karegoudra Jayanth, Benjamin Eysenbach
As unsupervised pretraining becomes increasingly ubiquitous in reinforcement learning, a more thorough theoretical understanding of these methods becomes of equal importance to the…
On the Role of Computation in Reinforcement Learning
Raj Ghugare, Michał Bortkiewicz, Alicja Ziarko +1
How does the amount of compute available to a reinforcement learning (RL) policy affect its learning? Can policies using a fixed amount of parameters, still benefit from additional…
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…
Is Temporal Difference Learning the Gold Standard for Stitching in RL?
Michał Bortkiewicz, Władysław Pałucki, Mateusz Ostaszewski +1
Reinforcement learning (RL) promises to solve long-horizon tasks even when training data contains only short fragments of the behaviors. This experience stitching capability is oft…
Consistent Zero-Shot Imitation with Contrastive Goal Inference
Kathryn Wantlin, Chongyi Zheng, Benjamin Eysenbach
Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction or gradient updates at test time.…
Value Flows
Perry Dong, Chongyi Zheng, Chelsea Finn +2
While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods exploit the return distribution to pr…
Contrastive Representations for Temporal Reasoning
Alicja Ziarko, Michal Bortkiewicz, Michal Zawalski +2
In classical AI, perception relies on learning state-based representations, while planning, which can be thought of as temporal reasoning over action sequences, is typically achiev…
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…
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…
Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
Patrik Reizinger, Bálint Mucsányi, Siyuan Guo +3
Self-supervised feature learning and pretraining methods in reinforcement learning (RL) often rely on information-theoretic principles, termed mutual information skill learning (MI…
Intention-Conditioned Flow Occupancy Models
Chongyi Zheng, Seohong Park, Sergey Levine +1
Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then can be used by anyone in the comm…
Horizon Reduction Makes RL Scalable
Seohong Park, Kevin Frans, Deepinder Mann +3
In this work, we study the scalability of offline reinforcement learning (RL) algorithms. In principle, a truly scalable offline RL algorithm should be able to solve any given prob…
Normalizing Flows are Capable Models for Continuous Control
Raj Ghugare, Benjamin Eysenbach
Modern reinforcement learning (RL) algorithms have found success by using powerful probabilistic models, such as transformers, energy-based models, and diffusion/flow-based models.…