Reinforcement learning agent learns how to perform a task by interacting with the environment. The use of reinforcement learning in real-life applications has been limited because of the sample efficiency problem. Interactive reinforcement learning has been developed to speed up the agent's learning and facilitate to learn from ordinary people by allowing them to provide social feedback, e.g, evaluative feedback, advice or instruction. Inspired by real-life biological learning scenarios, there could be many ways to provide feedback for agent learning, such as via hardware delivered, natural interaction like facial expressions, speech or gestures. The agent can even learn from feedback via unimodal or multimodal sensory input. This paper reviews methods for interactive reinforcement learning agent to learn from human social feedback and the ways of delivering feedback. Finally, we discuss some open problems and possible future research directions.

No abstract

No abstract

No abstract

No abstract