Comparer des méthodes

	Apprentissage par Renforcement Explicable	Mécanisme d'attention
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2018–2020	2015
Auteur d'origine	Puiutta, E. & Veith, E. M. S. P. (survey); broader XAI community	Bahdanau, D.; Luong, M.T.
Type	Hybrid approach (RL + explainability methods)	Neural attention layer (encoder-decoder)
Source fondatrice	Puiutta, E., & Veith, E. M. S. P. (2020). Explainable Reinforcement Learning: A Survey. In Machine Learning and Knowledge Extraction (CD-MAKE 2020), Lecture Notes in Computer Science, vol. 12279, pp. 77–95. Springer.	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.
Alias	XRL, interpretable reinforcement learning, transparent RL, explainable RL	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention
Apparentées	3	5
Résumé	Explainable Reinforcement Learning (XRL) augments standard reinforcement learning agents with methods that make their policies, decisions, and learned behaviors interpretable to humans. Rather than treating the policy as a black box, XRL produces post-hoc explanations or builds inherently transparent policies, enabling trust verification, debugging, and accountability in high-stakes automated decision-making.	The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.