← Back to Research Radar
Academic Publication Academic Publication

Explainable Reinforcement Learning: A Survey and Comparative Review

162
Citations
July 31, 2024
Published Date

Research Abstract & Technology Focus

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of reinforcement learning (RL) agents in sequential decision-making settings. Equipped with this information, practitioners can better understand important questions about RL agents (especially those deployed in the real world), such as what the agents will do and why. Despite increased interest, there exists a gap in the literature for organizing the plethora of papers—especially in a way that centers the sequential decision-making nature of the problem. In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting. We propose three high-level categories: feature importance, learning process and Markov decision process, and policy-level. We overview techniques according to this taxonomy, highlighting challenges and opportunities for future work. We conclude by using these gaps to motivate and outline a roadmap for future work.
explainable reinforcement learning survey comparative review
Read Full Literature

Correlated Market Trend: Adaptive Learning

Bridging academia to market: The 60-day public search velocity mapping directly to the core technology of this paper. Dashed line represents 7-day moving average.

AI Semantic Synergy Context

Connecting this academic literature to real-world market discussions and products.

crossref.org › academic paper
0%

Explainable Reinforcement Learning: A Survey and Comparative Review

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decis...
crossref.org › academic paper
0%

DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning

Abstract General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs)1,2 and ch...
roipad.com › trend story
0%

TinyLoRA – Learning to Reason in 13 Parameters

Recent research has shown that language models can learn to \textit{reason}, often via reinforcement learning. Some work even trains low-rank parameterizations for reasoning, but conventional LoRA ...
crossref.org › academic paper
0%

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the r...
crossref.org › academic paper
0%

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model ...

Frequently Asked Questions (FAQ)

Curated market intelligence mapped to this research.

What is the core focus of the research titled 'Explainable Reinforcement Learning: A Survey and Comparative Review'?

This literature focuses on: Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of reinforcement learning (RL) ...

Are there open-source GitHub repositories related to Explainable Reinforcement Learning: A Survey and Comparative Review?

Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.

Which startups are commercializing the technology behind Explainable Reinforcement Learning: A Survey and Comparative Review?

Products like Padel Chess are bringing this to market. Their focus is: Padel tactics learning app.

What other academic literature is closely related to 'Explainable Reinforcement Learning: A Survey and Comparative Review'?

Yes, highly correlated activity was mapped. An entry titled 'Explainable Reinforcement Learning: A Survey and Comparative Review' discusses this: Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recen...

Are there commercial applications of 'Explainable Reinforcement Learning: A Survey and Comparative Review' in market news publications?

Yes, highly correlated activity was mapped. An entry titled 'TinyLoRA – Learning to Reason in 13 Parameters' discusses this: Recent research has shown that language models can learn to \textit{reason}, often via reinforcement learning. Some work even trains low-rank param...

Cite this Market Intelligence Report

Reference our AI-mapped synergy between this research and the commercial market to instantly build authority.

Commercial Realization

Startups and Open Source tools heavily associated with the concepts explored in this paper.

Associated Media Narrative