用技能-环境贝叶斯网络自动设计强化学习训练课程，提升效率与性能。
Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network
- 构建技能-环境贝叶斯网络，建模技能、目标与环境特征关系
- 基于预测成功率动态选择下一任务，实现预期收益最大化
- 在网格世界、连续控制和机器人仿真中均优于基准方法
强化学习的一大挑战是自动生成训练课程以缩短训练时间或提升目标任务表现。本文提出SEBN（技能-环境贝叶斯网络），通过建模一组技能、与奖励结构相关的任务目标以及环境特征之间的概率关系，预测策略在（可能未见过的）任务上的表现。我们设计了一种算法，利用SEBN推断的代理成功概率，按预期改进程度对潜在下一任务进行加权选择。在离散网格世界、连续控制及模拟机器人三个环境中评估了该课程的效果，结果表明，基于SEBN构建的课程通常优于其他基线方法。
原文摘要 · Abstract (English)
A major challenge for reinforcement learning is automatically generating curricula to reduce training time or improve performance in some target task. We introduce SEBNs (Skill-Environment Bayesian Networks) which model a probabilistic relationship between a set of skills, a set of goals that relate to the reward structure, and a set of environment features to predict policy performance on (possibly unseen) tasks. We develop an algorithm that uses the inferred estimates of agent success from SEBN to weigh the possible next tasks by expected improvement. We evaluate the benefit of the resulting curriculum on three environments: a discrete gridworld, continuous control, and simulated robotics. The results show that curricula constructed using SEBN frequently outperform other baselines.
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