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Composing Value Functions in Reinforcement Learning
Benjamin van Niekerk · Steven James · Adam Earle · Benjamin Rosman

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #251

An important property for lifelong-learning agents is the ability to combine existing skills to solve new unseen tasks. In general, however, it is unclear how to compose existing skills in a principled manner. Under the assumption of deterministic dynamics, we prove that optimal value function composition can be achieved in entropy-regularised reinforcement learning (RL), and extend this result to the standard RL setting. Composition is demonstrated in a high-dimensional video game, where an agent with an existing library of skills is immediately able to solve new tasks without the need for further learning.

Author Information

Benjamin van Niekerk (University of the Witwatersrand)
Steven James (University of the Witwatersrand)
Adam Earle (University of the Witwatersrand)
Benjamin Rosman (Council for Scientific and Industrial Research)

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