论文标题
BAM：具有自适应记忆的贝叶斯
BAM: Bayes with Adaptive Memory
论文作者
论文摘要
通过贝叶斯定理在线学习允许将新数据连续集成到代理当前的信念中。但是，贝叶斯方法在非固定环境中的幼稚应用导致适应缓慢，并导致可能自信地收敛到错误的参数值的状态估计。在不断变化的环境中学习时的一个常见解决方案是丢弃/减轻过去的数据。但是，这种“忘记”的简单机制无法解释以下事实：许多现实世界环境都涉及重新审视类似状态。我们提出了一个新的框架，即具有自适应记忆（BAM）的贝叶斯，它通过允许代理商选择过去的观察值以及要忘记哪些观察值来利用过去的经验。我们证明，BAM概括了许多流行的贝叶斯更新规则，用于非平稳环境。通过各种实验，我们证明了BAM在不断变化的世界中不断适应的能力。
Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non stationary environments leads to slow adaptation and results in state estimates that may converge confidently to the wrong parameter value. A common solution when learning in changing environments is to discard/downweight past data; however, this simple mechanism of "forgetting" fails to account for the fact that many real-world environments involve revisiting similar states. We propose a new framework, Bayes with Adaptive Memory (BAM), that takes advantage of past experience by allowing the agent to choose which past observations to remember and which to forget. We demonstrate that BAM generalizes many popular Bayesian update rules for non-stationary environments. Through a variety of experiments, we demonstrate the ability of BAM to continuously adapt in an ever-changing world.
