Average Reward Reinforcement Learning

Prasad Tadepalli俄勒冈州立大学$77,772 - 12最多。平均奖励强化学习强化学习是通过奖励和惩罚来提高智能体的表现。到目前为止，对这一现象的大多数研究都假设，学习是通过最大化来自环境的折扣总奖励来进行的。折现倾向于牺牲较高的长期奖励来换取短期奖励。另一方面，在实际情况下，使用单位时间的平均奖励往往能更好地提高玩家的表现。一个这样的应用是自动引导车辆（agv -柔性制造系统中用于物料处理任务的机器人）的调度，其中贴现总奖励的优化将影响机器人为近的机器服务，而忽略远处的机器，导致较远的机器停止，甚至可能导致整个制造系统的停止。本项目的目标是利用AGV调度问题作为研究优化平均奖励的强化学习方法的手段，以避免此类陷阱，从而更好地理解在自动平均奖励强化学习下影响性能的因素。

IRI-9520243 Prasad Tadepalli Oregon State University $77,772 - 12 mos. Average Reward Reinforcement Learning Reinforcement learning is the improvement of performance of an agent due to rewards and punishments. Most studies of the phenomenon so far have assumed that the learning proceeds through maximizing the discounted total reward from the environment. Discounting tends to favor the sacrifice of higher long-term rewards in favor of short-term rewards. On the other hand, performance is often better enhanced in real situations by the use of average reward per unit time. One such application is the scheduling of automatic guided vehicles (AGVs - robots used for material handling tasks in flexible manufacturing systems), where optimization of discounted total reward will influence the robots to serve machines that are close and neglect those far away, leading to stoppage of the more distant machines, and perhaps to the halting of the entire manufacturing system. The objective of this project is to use the AGV scheduling problem as a means of studying reinforcement learning methods that optimize average reward to avoid such pitfalls, leading to a better understanding of the factors that influence performance under automated average reward reinforcement learning.