Interpretable experiential learning based on state history and global feedback
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917454069891072 |
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| author | Kolonin, Anton |
| author_facet | Kolonin, Anton |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00940 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Interpretable experiential learning based on state history and global feedback Kolonin, Anton Machine Learning Artificial Intelligence 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. |
| title | Interpretable experiential learning based on state history and global feedback |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.00940 |