23 citations · 101 across the 30 of their papers we have counts for
32 papers
A Unified Framework for Empowerment and Predictive Control
Wooyoung Chung, Tristan Shah, Volodymyr Makarenko +1
Sampling-based model predictive control (MPC) is a powerful approach to trajectory optimization, but its performance depends on an informative cost function that often requires sub…
Bellman Meets Lyapunov: Unsupervised Reinforcement Learning via Mastering Chaos
Tristan Shah, Wooyoung Chung, Volodomyr Makarenko +2
Reinforcement learning (RL) is a powerful paradigm for training agents, yet its success rests on domain expertise of human engineers who design informative reward signals for every…
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…
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…
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…
Decentralized Traffic Flow Optimization Through Intrinsic Motivation
Himaja Papala, Daniel Polani, Stas Tiomkin
Traffic congestion has long been an ubiquitous problem that is exacerbating with the rapid growth of megacities. In this proof-of-concept work we study intrinsic motivation, implem…
Acoustic Wave Manipulation Through Sparse Robotic Actuation
Tristan Shah, Noam Smilovich, Feruza Amirkulova +2
Recent advancements in robotics, control, and machine learning have facilitated progress in the challenging area of object manipulation. These advancements include, among others, t…
EVAL: EigenVector-based Average-reward Learning
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin +1
In reinforcement learning, two objective functions have been developed extensively in the literature: discounted and averaged rewards. The generalization to an entropy-regularized…
Average-Reward Soft Actor-Critic
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin +1
The average-reward formulation of reinforcement learning (RL) has drawn increased interest in recent years for its ability to solve temporally-extended problems without relying on…
Bootstrapped Reward Shaping
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin +1
In reinforcement learning, especially in sparse-reward domains, many environment steps are required to observe reward information. In order to increase the frequency of such observ…
SuPLE: Robot Learning with Lyapunov Rewards
Phu Nguyen, Daniel Polani, Stas Tiomkin
The reward function is an essential component in robot learning. Reward directly affects the sample and computational complexity of learning, and the quality of a solution. The des…
Boosting Soft Q-Learning by Bounding
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin +1
An agent's ability to leverage past experience is critical for efficiently solving new tasks. Prior work has focused on using value function estimates to obtain zero-shot approxima…
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…
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics
Tristan Shah, Feruza Amirkulova, Stas Tiomkin
Controlling systems governed by partial differential equations is an inherently hard problem. Specifically, control of wave dynamics is challenging due to additional physical const…
Dimensionality Reduction of Dynamics on Lie Manifolds via Structure-Aware Canonical Correlation Analysis
Wooyoung Chung, Daniel Polani, Stas Tiomkin
Incorporating prior knowledge into a data-driven modeling problem can drastically improve performance, reliability, and generalization outside of the training sample. The stronger…
Controllability-Constrained Deep Network Models for Enhanced Control of Dynamical Systems
Suruchi Sharma, Volodymyr Makarenko, Gautam Kumar +1
Control of a dynamical system without the knowledge of dynamics is an important and challenging task. Modern machine learning approaches, such as deep neural networks (DNNs), allow…
Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems
Derek Lilienthal, Paul Mello, Magdalini Eirinaki +1
While recommender systems have become an integral component of the Web experience, their heavy reliance on user data raises privacy and security concerns. Substituting user data wi…
Bounding the Optimal Value Function in Compositional Reinforcement Learning
Jacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas +2
In the field of reinforcement learning (RL), agents are often tasked with solving a variety of problems differing only in their reward functions. In order to quickly obtain solutio…
Leveraging Prior Knowledge in Reinforcement Learning via Double-Sided Bounds on the Value Function
Jacob Adamczyk, Stas Tiomkin, Rahul Kulkarni
An agent's ability to leverage past experience is critical for efficiently solving new tasks. Approximate solutions for new tasks can be obtained from previously derived value func…
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning
Jacob Adamczyk, Argenis Arriojas, Stas Tiomkin +1
In reinforcement learning (RL), the ability to utilize prior knowledge from previously solved tasks can allow agents to quickly solve new problems. In some cases, these new problem…
Intrinsic Motivation in Dynamical Control Systems
Stas Tiomkin, Ilya Nemenman, Daniel Polani +1
Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation. The computational principles underlying this behavior remain…
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…
Entropy Regularized Reinforcement Learning Using Large Deviation Theory
Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin +1
Reinforcement learning (RL) is an important field of research in machine learning that is increasingly being applied to complex optimization problems in physics. In parallel, conce…
GEM: Group Enhanced Model for Learning Dynamical Control Systems
Philippe Hansen-Estruch, Wenling Shang, Lerrel Pinto +2
Learning the dynamics of a physical system wherein an autonomous agent operates is an important task. Often these systems present apparent geometric structures. For instance, the t…