activity
20192026
most citedDynamics Generalization via Information Bottleneck in Deep Reinforcement Learning

17 citations · 19 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Multi-Agent Empowerment and Emergence of Complex Behavior in Groups

Tristan Shah, Ilya Nemenman, Daniel Polani +1

Intrinsic motivations are receiving increasing attention, i.e. behavioral incentives that are not engineered, but emerge from the interaction of an agent with its surroundings. In…

cs.AI2026

Emergence of Physical Intelligence via Controllable Information Production

Tristan Shah, Stas Tiomkin

Intrinsic Motivation (IM) aims to train agents without external rewards, enabling useful behavior to emerge from the agent's interaction with its environment alone. However, the do…

cs.AI2025

Goals and the Structure of Experience

Nadav Amir, Stas Tiomkin, Angela Langdon

Purposeful behavior is a hallmark of natural and artificial intelligence. Its acquisition is often believed to rely on world models, comprising both descriptive (what is) and presc…

cs.AI2024

Learning telic-controllable state representations

Nadav Amir, Stas Tiomkin

Computational models of purposeful behavior comprise both descriptive and prescriptive aspects, used respectively to ascertain and evaluate situations in the world. In reinforcemen…

cs.AI2022

Multi-Objective Policy Gradients with Topological Constraints

Kyle Hollins Wray, Stas Tiomkin, Mykel J. Kochenderfer +1

Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a c…

cs.AI2020

AvE: Assistance via Empowerment

Yuqing Du, Stas Tiomkin, Emre Kiciman +3

One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on i…