2020/06/26 by Khimya Khetarpal, Khetarpal, Khimya, Zafarali Ahmed +7 · 2 voices · 3 citations
Computer Science · Decision Sciences · Mathematics · #Artificial Intelligence (cs.AI) #Complex Systems and Decision Making #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2006.15085
openalex publication_date 2020/06/26 · arxiv published 2020/06/26 · arxiv updated 2020/06/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Reinforcement learning algorithms usually assume that all actions are always available to an agent. However, both people and animals understand the general link between the features of their environment and the actions that are feasible. Gibson (1977) coined the term "affordances" to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents. In this paper, we develop a theory of affordances for agents who learn and plan in Markov Decision Processes. Affordances play a dual role in this case. On one hand, they allow faster planning, by reducing the number of actions available in any given situation. On the other hand, they facilitate more efficient and precise learning of transition models from data, especially when such models require function approximation. We establish these properties through theoretical results as well as illustrative examples. We also propose an approach to learn affordances and use it to estimate transition models that are simpler and generalize better.