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Deep Reinforcement Learning (DRL) has recently made remarkable progress in solving complex tasks in several application domains, such as games, finance, autonomous driving, and recommendation systems. However, the black-box nature of deep neural networks and the complex interaction among various factors, such as the environment, reward policy, and state representation, raise challenges in understanding and interpreting DRL models’ decision-making processes. To address these issues, the workshop aims to explore the intersection of DRL with another important research area in artificial intelligence: Explainable Artificial Intelligence (XAI). XAI has become a crucial topic, aiming to enhance the accountability, trustworthiness, and accessibility of autonomous systems.

The workshop aims to bring together researchers, practitioners, and experts from both communities (DRL and XAI) by primarily focusing on methods, techniques, and frameworks that enhance the explainability and interpretability of DRL algorithms. Additionally, we will work towards defining standardized metrics and protocols to evaluate the performance and transparency of autonomous systems.

Call for Papers

We solicit submissions of previously unpublished papers, both as short and full papers. Short papers are up to 4 pages max without any supplemental material associated with. Full papers are up to 7 pages and can be associated with supplementary materials (unlimited pages for supplemental material) attached at the end of the manuscript. Note that looking at supplementary material is at the discretion of the reviewers. The references pages and the supplemental materials are not considered in the calculation of pages, so you can use unlimited references in both the cases.

Submissions have to be novel contributions covering any topic listed below. We don’t accept work that has been already accepted or published to other venues before the submission deadline, or that is presented at the main AAAI conference, including as part of an invited talk.

Papers must be submitted through the open review system (LINK)). This workshop is not archival. Therefore, papers submitted to the workshop can be submitted to future conferences (e.g. ICML, IJCAI) if the acceptance notification comes after the workshop date (February, 27).

We encourage the authors to link a anonymized repository containing the code to replicate the results inside the corpus of the paper. While this is not a mandatory requirement, it will be positively taken in account during the reviewing process and the selection of the contributed talks. You can use Anonymous Github or you can upload your repository on a service that allows anonymity (e.g. GDrive allows anonymous links).

Submissions must be in an anonymized paper format following the same template of the AAAI track (see HERE). They will undergo double-blind peer review. Any data included in the submission (paper, supplemental material, linked code) must be anonymized.

Accepted works will be presented as contributed talks or as posters depending on schedule constraints. It is mandatory that at least one of the authors will attend the workshop and present its work during the contributed talks and the poster session.

Important Dates

Submission system opens: Oct 15 11:59 PM GMT, 2023

Submission deadline: , Nov 15 Nov 21st 11:59 PM GMT, 2023 (Extended!!!)

Notification date: Dec 10 11:59 PM GMT, 2023

Workshop: Feb 27 09:00 AM GMT-7, 2023

Submission Link: OpenReview


The topics include but are not limited to:

Note: XAI methods applied to deep reinforcement learning models will be prefered for the selection of contribution do be presented during the workshop in case of bordline decisions.




Invited Speakers

Jerone Andrews
Jerone Andrews
Sony AI
Mark Riedl
Georgia Institute of Technology
Anurag Koul
Microsoft Research
Melinda Gervasio
SRI International



Program Committee


If you have any questions feel free to contact us at any of the following email addresses: