Researchers at The University of Texas at Austin and Cognizant AI Labs have developed an AI-driven system that leverages 175 ...

Patrick MacAlpine and Peter Stone.

Transfer Learning for Reinforcement Learning Domains: A Survey. Matthew E. Taylor and Peter Stone. Journal of Machine Learning Research, 10(1):1633–1685, 2009.

Transfer Learning for Reinforcement Learning on a Physical Robot. Samuel Barrett, Matt E. Taylor, and Peter Stone. In Ninth International Conference on Autonomous Agents and Multiagent Systems - ...

Reasoning about Hypothetical Agent Behaviours and their Parameters. Stefano Albrecht and Peter Stone. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems ...

UT Austin Villa RoboCup 3D Simulation Base Code Release. Patrick MacAlpine and Peter Stone. In Sven Behnke, Daniel D. Lee, Sanem Sariel, and Raymond Sheh, editors, RoboCup 2016: Robot Soccer World Cup ...

To Teach or not to Teach? Decision Making Under Uncertainty in Ad Hoc Teams. Peter Stone and Sarit Kraus. In The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), ...

Current approaches to learning cooperative multi-agent behaviors assumerelatively restrictive settings. In standard fully cooperative multi-agentreinforcement learning, the learning algorithm controls ...

Robustly cooperating with unseen agents and human partners presents significantchallenges due to the diverse cooperative conventions these partners may adopt.Existing Ad Hoc Teamwork (AHT) methods ...

The goal of this study is to explore which aspects of people’s analytical decision making are affected when ex- posed to music. To this end, we apply a stochastic sequen- tial model of simple ...

Exploiting full-body dynamics in feedback control can enhance the balancing capability of a legged system using various techniques such as Whole-Body Control (WBC) or Centroidal Momentum control.

Lion-\(\mathcal{K}\) [CLLL23] is a family of optimization algorithms developed to provide a theoretical foundation for the Lion optimizer, which was originally discovered via symbolic search [CLH+23].