Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces

Understand

While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data.

• One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers.
• Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations.
• Previous approaches have shown the usefulness of human input provided in this fashion (e.g., the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning.