Reinforcement Learning with Human and MDP Reward (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 techniques for combining human reward with RL from prior TAMER+RL work are tested on a second task, and these techniques' sensitivities to parameter changes are analyzed. Together, these examinations yield more general and prescriptive conclusions to guide others who wish to incorporate human knowledge into an RL algorithm. Second, TAMER+RL has thus far been limited to a sequential setting, in which training occurs before learning from MDP reward. In this paper, we introduce a novel algorithm that shares the same spirit as TAMER+RL but learns simultaneously from both reward sources, enabling the human feedback to come at any time during the reinforcement learning process. We call this algorithm simultaneous TAMER+RL. To enable simultaneous learning, we introduce a new technique that appropriately determines the magnitude of the human model's influence on the RL algorithm throughout time and state-action space.
In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), June 2012.

W. Bradley Knox bradknox [at] mit edu
Peter Stone pstone [at] cs utexas edu