6 accepted papers
Optimizing large-scale logistics is computationally challenging due to its scale and requirement to be robust to stochastic and time-varying weather disturbances. However, prior research in multi-agent reinforcement learning (MARL) does not address scenarios that capture complexity of logistics oper
Athletics are a quintessential and universal expression of humanity. From French monks who in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$12{\text{th}}$</tex-math></inline-formula> century invented <italic
High-performing human teams leverage intelligent and efficient communication and coordination strategies to collaboratively maximize their joint utility. Inspired by teaming behaviors among humans, I seek to develop computational methods for synthesizing intelligent communication and coordination st…
Extending recent advances in Learning from Demonstration (LfD) frameworks to multi-robot settings poses critical challenges such as environment non-stationarity due to partial observability which is detrimental to the applicability of existing methods. Although prior work has shown that enabling com…
Decision Transformers (DT) have drawn upon the success of Transformers by abstracting Reinforcement Learning as a target-return-conditioned, sequence modeling problem. In our work, we claim that the distribution of DT's target-returns represents a series of different tasks that agents must learn to…
Information sharing is key in building team cognition and enables coordination and cooperation. High-performing human teams also benefit from acting strategically with hierarchical levels of iterated communication and rationalizability, meaning a human agent can reason about the actions of their tea…