基于状态历史与全局反馈的可解释学习模型，能高效建模行为策略。
Interpretable experiential learning based on state history and global feedback
- 利用状态历史和全局反馈构建可解释的过渡图模型
- 在Atari Breakout上性能媲美主流神经网络方法
- 适合资源受限环境下的强化学习任务
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，能够以状态集合间的转移图形式学习行为模型，转移边带有效用值和证据计数。该模型适用于资源受限环境中的强化学习问题。在OpenAI Gym Atari Breakout基准上进行了全面评估，性能与某些已知的神经网络解决方案相当。
原文摘要 · Abstract (English)
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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