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. This can lead to diminished user trust, and makes for a more challenging verification of systems. To address this challenge, this paper introduces Explanations using Alternative Realities for Reinforcement Learning (EARL), a Python library to produce counterfactual explanations in RL settings. This library allows the user to produce explanations by exploring What-if scenarios to clarify agent behavior by comparing possible outcomes. Counterfactual explanations have been shown to be intuitive and user-friendly in psychology research, but have only recently been explored in RL, with existing implementations usually limited to toy examples and benchmarks. EARL supports counterfactual explanation generation in realistic RL-based self-adaptive systems. To demonstrate its applicability, we demonstrate its use in a simulation of CitiBikes, a self-adaptive bike-sharing system, and we provide evaluations showing how it performs in real applications.
This work reviews pre-2019 XRL approaches, categorizing them into policy explanation, reward decomposition, model transparency, and post-hoc interpretability methods, and proposes a framework that combines interpretable policies, surrogate models, attention mechanisms, and visualization techniques to enhance transparency without significantly reducing performance.
Michael Anderson, David Thompson· International Journal of Art...· 0 citations
This work employs Inductive Logic Programming (ILP) to extract symbolic representations of RL policies and define a novel set of explainability metrics, including activation rate, feature coverage, syntactic distance and semantic distance, which provide crucial insights for the transfer and generalization of action-specific policies.
From this literature review, it becomes evident, in face of a series of preliminary and promising studies, the field of XRL still lacks a proper accounting towards full explainability and it emerges that more effort should be devolved into developing paradigms with a human-in-the-loop factor and standardized metrics should be adopted to allow a fair comparison of different explanation methods.
Daniele Melloni, Andrea Zingoni· Data mining and knowledge di...· 0 citations
Self-Review Reinforcement Learning consistently outperforms the RLVR in final reward performance and achieves greater learning efficiency by successfully transforming feedback into behavioral improvement.
A Self-Evolutional single-agent/multi-agent Reinforcement Learning (SE-RL) framework that utilizes a Large Language Model (LLM) to design various RL algorithm modules, such as agent model design, reward function, profiling, communication, and state imagination, by leveraging the LLM generating module output or code.
Vincent Fu, Xinxin Xu, Weichen Xu et al.· Proceedings of the 32nd ACM...· 0 citations
The aim of this paper is to describe the potential autonomous advancements the next generations of large language models may evolve and want to offer some suggestions as a theoretical and a technical framework.
Mengbo Song· Mathematical Modeling and Al...· 0 citations
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