Cycle-of-Learning for Autonomous Systems from Human Interaction

Abstract

We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms. We provide a taxonomy to categorize the types of human interaction and present our Cycle-of-Learning framework for autonomous systems that combines different human-interaction modalities with reinforcement learning. Two key concepts provided by our Cycle-of-Learning framework are how it handles the integration of the different human-interaction modalities (demonstration, intervention, and evaluation) and how to define the switching criteria between them.

Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606)

References in corpus (2)

• Deep reinforcement learning from human preferences
• Trial without Error: Towards Safe Reinforcement Learning via Human Intervention

Cited by in corpus (3)

• Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
• Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems
• Learning from Interventions using Hierarchical Policies for Safe Learning