Apprenticeship Learning for Initial Value Functions in Reinforcement Learning
Frédéric Maire, Vadim Bulitko · 2005
Reinforcement Learning has had spectacular successes over the last several decades. While meant to require less human input than supervised learning, reinforcement learning can be substantially accelerated with a priori available domain expertise. The ways of providing human knowledge to a reinforcement learning agent vary from crafting state features to initial policy design to initial value function design. We chose the latter and propose a novel approach for acquiring a high-quality initial value function via apprenticeship learning. This approach works well in domain when a body of expert data are available. Our apprentice reinforcement learning (ARL) agent uses dynamic programming