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

by   Garrett Warnell, et al.

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. In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer. We demonstrate Deep TAMER's success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling - a task that has proven difficult for even state-of-the-art reinforcement learning methods.



page 1

page 2

page 3

page 4


FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback

Reinforcement learning has been successful in training autonomous agents...

Deep Reinforcement Learning from Policy-Dependent Human Feedback

To widen their accessibility and increase their utility, intelligent age...

Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach

Deep Reinforcement Learning (DRL) has become a powerful methodology to s...

Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks

Deep Reinforcement Learning (DRL) has become a powerful strategy to solv...

On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning

In autonomous embedded systems, it is often vital to reduce the amount o...

Safe Deep RL in 3D Environments using Human Feedback

Agents should avoid unsafe behaviour during both training and deployment...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.