A Hybrid Connectionist/LCS for Hidden-state Problems

This data includes the evaluation of TRACA with other learning algorithms two maze navigation tasks. Specifically comparisons are made with other LCS based approaches including XCSMH and AgentP. Each algorithm is evaluated using a maze navigation task that has been identified as among the most difficult due to recurring aliased regions. The comparisons between algorithms include training time, test performance, and the size of the learned rule sets. TRACA is also tested on two variations of a truck driving task where it must learn to navigate four lanes of slower vehicles whilst avoiding collisions.

本数据集涵盖TRACA与其他学习算法在两项迷宫导航任务中的性能对比评估。具体而言，对比对象涵盖基于学习分类器系统（Learning Classifier System，LCS）的多种方法，包括XCSMH与AgentP。所有算法均依托一项因存在重复性混叠状态区域而被公认为极具挑战性的迷宫导航任务完成性能测试。算法间的对比维度包含训练时长、测试性能以及习得规则集的规模。此外，TRACA还在两类卡车驾驶任务变体中接受了测试，其需学会在四车道慢速车流中完成导航并规避碰撞。