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Found 7 papers, 5 papers with code
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their adoption, particularly in critical applications where the decisions an agent makes can have significant consequences.
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning
We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment.
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
In this paper, we extend a recently-proposed framework for explainable RL that is based on analyses of "interestingness."
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments
We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments that maximizes information about entities present in that space.
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 help explain its behavior.
