aiqbench analysis
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)
Full text under the publisher's open licence · 142 words
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