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Interactive Learning from Policy-Dependent Human Feedback

2017-01-21 · ICML 2017 8 · James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang, David Roberts, Matthew E. Taylor, Michael L. Littman

This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has made the assumption that people provide feedback for decisions that is dependent on the behavior they are teaching and is independent from the learner's current policy. We present empirical results that show this assumption to be false -- whether human trainers give a positive or negative feedback for a decision is influenced by the learner's current policy. Based on this insight, we introduce {\em Convergent Actor-Critic by Humans} (COACH), an algorithm for learning from policy-dependent feedback that converges to a local optimum. Finally, we demonstrate that COACH can successfully learn multiple behaviors on a physical robot.

📄 PDF Abstract BibTeX arXiv:1701.06049

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Policy Shaping: Integrating Human Feedback with Reinforcement Learning

2013-12-01 · NeurIPS 2013 12 · Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L. Isbell 외

A long term goal of Interactive Reinforcement Learning is to incorporate non-expert human feedback to solve complex tasks. State-of-the-art methods have approached this problem by mapping human information to reward and …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

2021-06-09 · Kimin Lee, Laura Smith, Pieter Abbeel

Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet easy enough to provide. Human-in-the-loop R…

reinforcement-learning Reinforcement Learning (RL)Unsupervised Pre-training

Influencing Reinforcement Learning through Natural Language Guidance

2021-04-04 · Tasmia Tasrin, Md Sultan Al Nahian, Habarakadage Perera, Brent Harrison

Interactive reinforcement learning agents use human feedback or instruction to help them learn in complex environments. Often, this feedback comes in the form of a discrete signal that is either positive or negative. Whi…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Multi-trainer Interactive Reinforcement Learning System

2022-10-14 · Zhaori Guo, Timothy J. Norman, Enrico H. Gerding

Interactive reinforcement learning can effectively facilitate the agent training via human feedback. However, such methods often require the human teacher to know what is the correct action that the agent should take. In…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Interactive Reinforcement Learning for Table Balancing Robot

2021-08-01 · ACL (splurobonlp) 2021 8 · Haein Jeon, Yewon Kim, Bo-Yeong Kang

With the development of robotics, the use of robots in daily life is increasing, which has led to the need for anyone to easily train robots to improve robot use. Interactive reinforcement learning(IARL) is a method for …

Automatic Speech Recognition Automatic Speech Recognition (ASR)Deep Reinforcement Learning reinforcement-learning+5