analiza aiqbench
This paper proposes an interpretable reinforcement learning framework that models agent behavior as a transition graph weighted by utility and evidence, offering a transparent alternative to opaque neural networks. Evaluated on the Atari Breakout benchmark, the approach achieves performance comparable to standard deep learning solutions while operating effectively in resource-constrained settings. The work advances the field of machine learning by prioritizing model interpretability alongside competitive empirical results.
Interpretable experiential learning based on state history and global feedback
arXiv:2605.00940v2 Announce Type: replace Abstract: 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.
Abstract: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.
5 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2605.00940 [cs.LG]
(or arXiv:2605.00940v2 [cs.LG] for this version)
From: Anton Kolonin Dr. [view email] [v1] Fri, 1 May 2026 04:22:16 UTC (2,916 KB) [v2] Fri, 16 Oct 2026 16:12:02 UTC (2,916 KB)
Text integral sub licența deschisă a publicației · 142 cuvinte
Subiecte conexe
Vertical Advantage: Transforming Industries with Lakebase and Agentic AI
Databricks · 27 aug. 2026
Artmarket.com: H1 2026 Financial Report -- One-of-a - Kind AI Systems Intuitive Artmarket and Blind Spot Will Drive the Next Phase of Growth for
australiannews.net · 29 sept. 2026
Artmarket.com: H1 2026 Financial Report -- One-of-a - Kind AI Systems Intuitive Artmarket® and Blind Spot Will Drive the Next Phase of Growth for AI - First Artprice in 2026 - 2030
thailand-business-news.com · 28 sept. 2026
OpenRouter: from Seed to Stripe — with OpenRouter’s Alex Atallah & AMP’s Anjney Midha
Latent Space · 25 sept. 2026