1 citations · 2 across the 5 of their papers we have counts for
6 papers
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies
Pedro Sequeira, Vidyasagar Sadhu, Melinda Gervasio
In this paper we present ToMCAT (Theory-of-Mind for Cooperative Agents in Teams), a new framework for generating ToM-conditioned trajectories. It combines a meta-learning mechanism…
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
Pedro Sequeira, Melinda Gervasio
In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-d…
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning
Haochen Wu, Pedro Sequeira, David V. Pynadath
We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagen…
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
Pedro Sequeira, Jesse Hostetler, Melinda Gervasio
In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-d…
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments
J. Brian Burns, Aravind Sundaresan, Pedro Sequeira +1
We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments that maximizes information abou…
A Framework for Understanding and Visualizing Strategies of RL Agents
Pedro Sequeira, Daniel Elenius, Jesse Hostetler +1
Recent years have seen significant advances in explainable AI as the need to understand deep learning models has gained importance with the increased emphasis on trust and ethics i…