Article

Explaining the Behaviour of Reinforcement Learning Agents in a Multi-Agent Cooperative Environment Using Policy Graphs

2024-01-19

Abstract excerpt

The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well-informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 02e5411d-8906-5256-ac3e-cd9a193e4148
DOI: 10.20944/preprints202401.1421.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Explaining the Behaviour of Reinforcement Learning Agents in a Multi-Agent Cooperative Environment Using Policy GraphsDOI 10.20944/preprints202401.1421.v1Read

A Policy-Graph Approach to Explain Reinforcement Learning Agents: A Novel Policy-Graph Approach with Natural Language and Counterfactual Abstractions for Explaining Reinforcement Learning AgentsSemantic proximity Read

EXPLAINABLE REINFORCEMENT LEARNING FOR TRANSPARENT AUTONOMOUS DECISION SYSTEMSSemantic proximity Read

CAUSAL EXPLAINABLE REINFORCEMENT LEARNING FOR RELIABLE AUTONOMOUS DECISION-MAKING IN DYNAMIC ENVIRONMENTSSemantic proximity Read

Learning from Explanations With Maximum Likelihood Inverse Reinforcement LearningSemantic proximity Read

Agentic AI: A Review, Applications, and Open Research ChallengesSemantic proximity Read

Learning Safe Behaviour via Justified Human Preferences and Hypothetical QueriesSemantic proximity Read

Counterfactual Explanations as Interventions in Latent SpaceSemantic proximity Read

Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human-Machine Teams (HMT)Semantic proximity Read

Exploring Simulated Morphic Fields for Sustainable Multi-Agent Learning and EducationSemantic proximity Read

How Do You Know That You Don't Know?Semantic proximity Read

Single-Thinking-Process-Based Learning of Complete Issue-Related Materials for Autonomous AgentsSemantic proximity Read