arXiv cs.AI· Anton Kolonin·· 12 小时前AI 评分22
基于状态历史与全局反馈的可解释经验学习模型
Interpretable experiential learning based on state history and global feedback
AI 导读
研究者提出一种基于状态历史与全局反馈的可解释经验学习模型，可学习由状态集间转移图表示的行为模型，转移附带效用与证据计数。该模型在 OpenAI Gym Atari Breakout 基准上评估，性能与部分已知的神经网络方案相当，预期适用于资源受限环境下的强化学习问题。
正文 · 原文
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.
| Comments: | 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 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.00940 [cs.LG] |
| (or arXiv:2605.00940v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00940 arXiv-issued DOI via DataCite |
Submission history
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)
来源：arXiv cs.AI · arxiv.org