Human AI Interaction Loop Training: New Approach For Interactive Reinforcement Learning

Abstract

Reinforcement Learning (RL) in various decision-making tasks of machine learning provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. This complexity, coming from high dimensionality and continuousness of the environments considered herein, calls for a large number of learning trials to learn about the environment through Reinforcement Learning. Imitation Learning (IL) offers a promising solution for those challenges using a teacher. In IL, the learning process can take advantage of human-sourced assistance and/or control over the agent and environment. A human teacher and an agent learner are considered in this study. The teacher takes part in the agent training towards dealing with the environment, tackling a specific objective, and achieving a predefined goal. Within that paradigm, however, existing IL approac

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keynavidi2020human

Related papers

• Improving Multimodal Interactive Agents With Reinforcement Learning From Human Feedback (2022)0.00
• Provably Feedback-efficient Reinforcement Learning Via Active Reward Learning (2023)0.00
• Perspectives On The Social Impacts Of Reinforcement Learning With Human Feedback (2023)0.00
• Improving Interactive Reinforcement Learning: What Makes A Good Teacher? (2019)11.19
• Human Decision Makings On Curriculum Reinforcement Learning With Difficulty Adjustment (2022)0.00
• Assessing Human Interaction In Virtual Reality With Continually Learning Prediction Agents Based On Reinforcement Learning Algorithms: A Pilot Study (2021)0.00
• A Hierarchical Approach To Population Training For Human-ai Collaboration (2023)0.00
• Userrl: Training Interactive User-centric Agent Via Reinforcement Learning (2025)0.00