Interpretable experiential learning based on state history and global feedback

Anton Kolonin

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arXiv · 2605.00940

Interpretable experiential learning based on state history and global feedback

Anton Kolonin

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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.

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Anton Kolonin. 2026-05-01. Interpretable experiential learning based on state history and global feedback. https://arxiv.org/abs/2605.00940

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