Using Deep Reinforcement Learning to Solve Perspective-Taking Task
Name
Aqeel Labash
Abstract
Perspective taking is the faculty that allows us to take the point of view of another agent.
This capability is not unique to humans. This is an essential ability for agents to achieve efficient social interactions, including cooperation and competition. In this work, we present our progress toward reverse engineering how perspective taking task might be accomplished in our brains. We introduce an environment designed from scratch for the purpose of creating perspective-taking tasks, in which the environment is partially observable by its agents. We also show a set of different models that were able to pass multiple tests that would benefit from perspective taking capabilities. These models were
trained using reinforcement learning algorithms assisted by artificial neural networks.
This capability is not unique to humans. This is an essential ability for agents to achieve efficient social interactions, including cooperation and competition. In this work, we present our progress toward reverse engineering how perspective taking task might be accomplished in our brains. We introduce an environment designed from scratch for the purpose of creating perspective-taking tasks, in which the environment is partially observable by its agents. We also show a set of different models that were able to pass multiple tests that would benefit from perspective taking capabilities. These models were
trained using reinforcement learning algorithms assisted by artificial neural networks.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Raul Vicente, Jaan Aru, Tambet Matiisen, Ardi Tampuu
Defence year
2017