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Interpretable experiential learning based on state history and global feedback

arXiv 2605.00940 ↗First posted 1 May 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

A new interpretable experiential learning model based on state history and global feedback is presented. It is capable of learning a behavioral model represented by a transition graph between sets of states, with transitions attributed with utility and evidence count. This model is expected to be suitable for solving reinforcement learning problem in resource-constrained environments. The model was thoroughly evaluated on the OpenAI Gym Atari Breakout benchmark, demonstrating performance comparable to some known neural network-based solutions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 1 May 2026.

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References · 8

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  3. 2023Explainable Deep Reinforcement Learning: State of the Art and Challengescited by 33
  4. 2024Local vs. Global Interpretability: A Computational Complexity Perspectivecited by 23
  5. 2023Process Mining for Unstructured Data: Challenges and Research Directionscited by 4
  6. 2025A Survey of Reinforcement Learning for Optimization in Automationcited by 4
  7. 2025Benchmarking In-context Experiential Learning Through Repeated Product Recommendationscited by 1
  8. 2025Marti-5: A Mathematical Model of "Self in the World" as a First Step Toward Self-Awarenesscited by 1

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  1. 2026LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks
  2. 2020Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data
  3. 2026Experiential Reinforcement Learning
  4. 2024Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop
  5. 2025Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approach
  6. 2026Learning to Coach for Experiential Learning
  7. 2019Continual Learning Using World Models for Pseudo-Rehearsal
  8. 2026LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure