Using Logical Specifications for Multi-Objective Reinforcement Learning

BYU ScholarsArchive,Kolby Nottingham

semanticscholar(2020)

引用 1|浏览3
暂无评分
摘要
USING LOGICAL SPECIFICATIONS FOR MULTI-OBJECTIVE REINFORCEMENT LEARNING Kolby Nottingham Computer Science Department Bachelor of Science In the multi-objective reinforcement learning (MORL) paradigm, the relative importance of environment objectives is often unknown prior to training, so agents must learn to specialize their behavior to optimize different combinations of environment objectives that are specified post-training. These are typically linear combinations, so the agent is effectively parameterized by a weight vector that describes how to balance competing environment objectives. However, we show that behaviors can be successfully specified and learned by much more expressive non-linear logical specifications. We test our agent in several environments with various objectives and show that it can generalize to many never-before-seen specifications.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络