What can I do here? A Theory of Affordances in Reinforcement Learning
By Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup. 2020
64 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Management Science and Operations Research
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In Artificial Intelligence
Citations in the corpus
- The Role of Executive Function in Shaping Reinforcement Learning. (2020, methodology): “This concept has recently been proposed as a novel method for making RL more efficient in complex state-spaces [50].”