Reinforcement learning from simultaneous human and MDP reward
W. Bradley Knox, Peter Stone · 2012
As computational agents are increasingly used beyond research labs, their success will depend on their ability to learn new skills and adapt to their dynamic, complex environments. If human users—without programming skills— can transfer their task knowledge to agents, learning can accelerate dramatically, reducing costly trials. The tamer framework guides the design of agents whose behavior can be shaped through signals of approval and disapproval, a natural form of human feedback. More recently, tamer+rl was introduced to enable human feedback to augment a traditional reinforcement learning (RL) agent that learns from a Markov decision process’s (MDP) reward signal. We address limitations of prior work on tamer and tamer+rl, contributing in two critical directions. First, the four successful