arXiv AI
Jun 16
Explainable deep learning improves human mental models of self-driving cars
arXiv:2411. 18714v3 Announce Type: replace-cross Abstract: Self-driving cars increasingly rely on deep neural networks to achieve human-like driving.
Explaining Reinforcement Learning Decisions in Self-adaptive Systems
arXiv:2608. 14620v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural networks, lack transparency and are difficult to understand.
Related stories
arXiv Machine Learning
Jul 23
Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation
arXiv:2509. 14925v2 Announce Type: replace Abstract: Deep reinforcement learning (DRL) methods, though powerful, often lack transparency, which limits their adoption in critical domains.
arXiv Machine Learning
Jul 31
Class-Aware Reinforcement Learning for Counterfactual Explanation Generation
arXiv:2607. 27905v1 Announce Type: new Abstract: Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome.
Hugging Face Trending Papers
Jul 30
Class-Aware Reinforcement Learning for Counterfactual Explanation Generation
Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome. Reinforcement learning (RL) offers a promising approach for CFE generation, enabling efficient exploration of counterfactual instances while ensuring control over key metrics like validity, sparsity, and proximity.
arXiv AI
Aug 24
Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes
The paper investigates how human observers interpret the learning processes of reinforcement learning (RL) agents. Using a novel observation-based paradigm, the authors conducted two experiments: an exploratory interview study with nine participants that identified four core themes—Agent Goals, Knowledge, Decision Making, and Learning Mechanisms—and a confirmatory study with 34 participants that applied the paradigm across navigation and manipulation tasks and two RL algorithms. Analyses of 816 responses validated the paradigm’s reliability and refined the thematic framework, showing how these themes evolve over time and interrelate.
arXiv AI
Jul 24
Explainability Framework for Policy-Aware Autonomous Agents
arXiv:2607. 21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal.
arXiv Machine Learning
Oct 8
A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior
arXiv:2602.02639v2 Announce Type: replace-cross Abstract: LLM self-explanations are often presented as a promising tool for AI oversight, yet their faithfulness to the model's true reasoning process...
arXiv AI
5d ago
A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs
A survey paper titled "A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs" reviews over 250 studies in the sub‑domain of eXplainable AI focused on reinforcement learning (XRL). The authors introduce a taxonomy based on the questions "What" (the target of explanation) and "How" (the method of explanation) and use it to organize the state‑of‑the‑art literature. They also highlight related domains that warrant further attention and outline key needs for advancing the field of XRL.
Hugging Face Trending Papers
Jun 23
Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Training safe Reinforcement Learning (RL) systems is inherently challenging, with no guarantee of avoiding unwanted behaviors. The most effective defenses against this are (i) transparency through explainability and (ii) alignment via human feedback.
Hugging Face Trending Papers
Aug 6
ProDVI: Programmatic Dynamics Priors for Value Network Initialization
Deep Reinforcement Learning (RL) is notoriously sample inefficient. One contributing factor is that RL agents are typically initialized from scratch, forcing them to acquire task-relevant knowledge through online interaction.
arXiv Machine Learning
Sep 22
RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner
arXiv:2410.23912v3 Announce Type: replace-cross Abstract: The reasoning abilities of large language models (LLMs) have improved with chain-of-thought (CoT) prompting, allowing models to solve complex...
arXiv AI
Sep 15
Specifying Reward Functions for RL Without Environment Sampling
arXiv:2609.15544v1 Announce Type: cross Abstract: Enabling human stakeholders to specify reward functions that lead to their desired outcomes is a key challenge in deploying reinforcement learning ag...