Author: Pedro Sequeira
-
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies
We introduce ToMCAT, a framework that integrates Theory-of-Mind (ToM) reasoning with a multiagent diffusion model to generate team-aware plans conditioned on agents’ goals and inferred teammate characteristics. Experiments in a simulated domain demonstrate that agents with more ToM capabilities outperform ones without ToM and that ToMCAT’s dynamic replanning mechanism significantly improves resource efficiency while maintaining…
-
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
Our tool provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms, natively supporting the popular RLLib toolkit.
-
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning
We approach the problem of understanding how people interact with each other in collaborative settings via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team’s behavior during some task.
-
Learning Sensor Control for Information Gain in Dynamic, Partially Observed and Sparsely Sampled Environments
We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments.
-
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
Our new framework provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms.
-
A Framework for understanding and Visualizing Strategies of RL Agents
We present a framework for learning comprehensible models of sequential decision tasks in which agent strategies are characterized using temporal logic formulas.
-
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space
We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characterize agent behavior.
-
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents’ Capabilities and Limitations
We propose an explainable reinforcement learning (XRL) framework that analyzes an agent’s history of interaction with the environment to extract interestingness elements that explain its behavior.
-
Interestingness Elements for Explainable Reinforcement Learning through Introspection
The framework uses introspective analysis of an agent’s history of interaction with its environment to extract several interestingness elements regarding its behavior.