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We describe a method to use discrete human feedback to enhance theperformance of deep learning agents in virtual three-dimensional environmentsby extending deep-reinforcement learning to model the confidence andconsistency of human feedback. This enables deep reinforcement learningalgorithms to determine the most appropriate time to listen to the humanfeedback, exploit the current policy model, or explore the agent's environment.Managing the trade-off between these three strategies allows DRL agents to berobust to inconsistent or intermittent human feedback. Through experimentationusing a synthetic oracle, we show that our technique improves the trainingspeed and overall performance of deep reinforcement learning in navigatingthree-dimensional environments using Minecraft. We further show that ourtechnique is robust to highly innacurate human feedback and can also operatewhen no human feedback is given.

Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds