有效运动技能学习的终点效应探索驱动力
End-Effect Exploration Drive for Effective Motor Learning
摘要：
基于强化学习的一个关键目标是反转效应的目标分布的想法，提出了在缺乏明确的前向模型的情况下，末端效应驱动作为实现目标导向运动学习的有效方式。终端效应模型依赖于对当前政策影响的简单统计记录，在此用作对资源要求更高的前瞻性模型的替代。当与奖励结构相结合时，它形成了轻量级变分自由能最小化设置的核心。主要的困难在于维持这种简化的效果模型以及在线更新政策。当先验目标分布均匀时，它提供了一种学习有效探索策略的方法，与内在好奇心原则一致。当与外部奖励相结合时，我们的方法最终被证明比传统的离线策略技术提供更快的训练。
英文摘要：
Stemming on the idea that a key objective in reinforcement learning is to invert a target distribution of effects, end-effect drives are proposed as an effective way to implement goal-directed motor learning, in the absence of an explicit forward model. An end-effect model relies on a simple statistical recording of the effect of the current policy, here used as a substitute for the more resource-demanding forward models. When combined with a reward structure, it forms the core of a lightweight variational free energy minimization setup. The main difficulty lies in the maintenance of this simplified effect model together with the online update of the policy. When the prior target distribution is uniform, it provides a ways to learn an efficient exploration policy, consistently with the intrinsic curiosity principles. When combined with an extrinsic reward, our approach is finally shown to provide a faster training than traditional off-policy techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
