强化学习中的系统不确定性模型
A model for system uncertainty in reinforcement learning
摘要：
这一工作为研究不确定环境下的连续时间控制问题提供了一个严格的框架。该框架将状态动力学中的模型不确定性视为函数空间上的度量。随着代理学习其环境，该度量被认为会随着时间的推移而改变。该模型可以看作是贝叶斯强化学习或适应控制的一个变体。我们研究了局部最优轨迹的必要条件，特别是推导出一个适当的动态规划原则和Hamilton-Jacobi方程。该模型为研究强化学习中探索和利用之间的权衡提供了一个可能的框架。
英文摘要：
This work provides a rigorous framework for studying continuous time control problems in uncertain environments. The framework considered models uncertainty in state dynamics as a measure on the space of functions. This measure is considered to change over time as agents learn their environment. This model can be seem as a variant of either Bayesian reinforcement learning or adaptive control. We study necessary conditions for locally optimal trajectories within this model, in particular deriving an appropriate dynamic programming principle and Hamilton-Jacobi equations. This model provides one possible framework for studying the tradeoff between exploration and exploitation in reinforcement learning.
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。
