Poster
Composing Value Functions in Reinforcement Learning
Benjamin van Niekerk · Steven James · Adam Earle · Benjamin Rosman
Pacific Ballroom #251
Keywords: [ Transfer and Multitask Learning ] [ Theory and Algorithms ] [ Deep Reinforcement Learning ]
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.
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