Accelerating Reinforcement Learning Agent With Eeg-based Implicit Human Feedback

Abstract

Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning. However, previous methods require human observer to give inputs explicitly (e.g., press buttons, voice interface), burdening the human in the loop of RL agent's learning process. Further, it is sometimes difficult or impossible to obtain the explicit human advise (feedback), e.g., autonomous driving, disabled rehabilitation, etc. In this work, we investigate capturing human's intrinsic reactions as implicit (and natural) feedback through EEG in the form of error-related potentials (ErrP), providing a natural and direct way for humans to improve the RL agent learning. As such, the human intelligence can be integrated via implicit feedback with RL algorithms to accelerate the learning of RL agent. We develop three reasonably complex 2D discrete navigational games to experimentally evaluate the overall performance of the proposed work. Major contributions of our work are

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyxu2020accelerating

Related papers

• Aligning Humans And Robots Via Reinforcement Learning From Implicit Human Feedback (2025)2.26
• Towards Reinforcement Learning From Neural Feedback: Mapping Fnirs Signals To Agent Performance (2025)0.00
• Improving Multimodal Interactive Agents With Reinforcement Learning From Human Feedback (2022)0.00
• Explore, Exploit Or Listen: Combining Human Feedback And Policy Model To Speed Up Deep Reinforcement Learning In 3D Worlds (2017)0.00
• Can You See How I Learn? Human Observers' Inferences About Reinforcement Learning Agents' Learning Processes (2025)0.00
• Mapping Out The Space Of Human Feedback For Reinforcement Learning: A Conceptual Framework (2024)0.00
• A Survey On Enhancing Reinforcement Learning In Complex Environments: Insights From Human And LLM Feedback (2024)0.00
• Ego-foresight: Self-supervised Learning Of Agent-aware Representations For Improved RL (2024)0.00