Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework

Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this paper, we investigate the potential of agent learning from trainers’ facial expressions via...

Facial feedback for reinforcement learning: a case study and offline analysis using the TAMER framework

Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this article, we investigate the potential of agent learning from trainers’ facial expressions via...

Social interaction for efficient agent learning from human reward

Learning from rewards generated by a human trainer observing an agent in action has been proven to be a powerful method for teaching autonomous agents to perform challenging tasks, especially for those non-technical users. Since the efficacy of this approach depends critically on the reward the trainer provides, we consider how the...

Leveraging Social Networks to Motivate Humans to Train Agents (Extended Abstract)

Learning from rewards generated by a human trainer ob- serving the agent in action has been demonstrated to be an effective method for humans to teach an agent to perform challenging tasks. However, how to make the agent learn most efficiently from these kinds of human reward is still under-addressed. In this paper, we investigate the effect of...