Autonomous agents must often detect affordances: the set of behaviors enabled by a situation. Affordance detection is particularly helpful in domains with large action spaces, allowing the agent to prune its search space by avoiding futile behaviors. This paper presents a method for affordance extraction via word embeddings trained on a Wikipedia corpus. The resulting word vectors are treated as a common knowledge database which can be queried using linear algebra. We apply this method to a reinforcement learning agent in a text-only environment and show that affordancebased action selection improves performance most of the time. Our method increases the computational complexity of each learning step but significantly reduces the total number of steps needed. In addition, the agent's action selections begin to resemble those a human would choose.
REF studied affordances, the set of behaviors enabled by a situation, and presented a method for affordance extraction via inner products of pre-trained word embeddings.
What can you do with a rock? Affordance extraction via word embeddings
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), Pages 1039-1045, 2017
Computer Science