Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning

2020-10-15 · NeurIPS Workshop HAMLETS 2020 12 · Anonymous

Human-in-the-loop Reinforcement Learning (HRL) aims to integrate human guidance with Reinforcement Learning (RL) algorithms to improve sample efficiency and performance. A common type of human guidance in HRL is binary evaluative "good" or "bad" feedback for queried states and actions. However, this type of learning scheme suffers from the problems of weak supervision and poor efficiency in leveraging human feedback. To address this, we present EXPAND (EXPlanation AugmeNted feeDback) which provides a visual explanation in the form of saliency maps from humans in addition to the binary feedback. EXPAND employs a state perturbation approach based on salient information in the state to augment the binary feedback. We choose five tasks, namely Pixel-Taxi and four Atari games, to evaluate this approach. We demonstrate the effectiveness of our method using two metrics: environment sample efficiency and human feedback sample efficiency. We show that our method significantly outperforms previous methods. We also analyze the results qualitatively by visualizing the agent's attention. Finally, we present an ablation study to confirm our hypothesis that augmenting binary feedback with state salient information results in a boost in performance.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Atari Games reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

2020-06-26 · NeurIPS 2021 12 · Lin Guan, Mudit Verma, Sihang Guo, Ruohan Zhang 외

Human explanation (e.g., in terms of feature importance) has been recently used to extend the communication channel between human and agent in interactive machine learning. Under this setting, human trainers provide not …

Atari Games Data Augmentation Deep Reinforcement Learning Feature Importance+3

Accelerating the Learning of TAMER with Counterfactual Explanations

2021-08-03 · Jakob Karalus, Felix Lindner

The capability to interactively learn from human feedback would enable agents in new settings. For example, even novice users could train service robots in new tasks naturally and interactively. Human-in-the-loop Reinfor…

counterfactual reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Tell me why: Training preferences-based RL with human preferences and step-level explanations

2024-05-23 · Jakob Karalus

Human-in-the-loop reinforcement learning allows the training of agents through various interfaces, even for non-expert humans. Recently, preference-based methods (PbRL), where the human has to give his preference over tw…

Human-in-the-Loop Systems for Adaptive Learning Using Generative AI

2025-08-14 · Bhavishya Tarun, Haoze Du, Dinesh Kannan, Edward F. Gehringer arxiv

A Human-in-the-Loop (HITL) approach leverages generative AI to enhance personalized learning by directly integrating student feedback into AI-generated solutions. Students critique and modify AI responses using predefine…

Prompt Engineering

An Augmented Reality Platform for Introducing Reinforcement Learning to K-12 Students with Robots

2021-10-10 · Ziyi Zhang, Samuel Micah Akai-Nettey, Adonai Addo, Chris Rogers 외

Interactive reinforcement learning, where humans actively assist during an agent's learning process, has the promise to alleviate the sample complexity challenges of practical algorithms. However, the inner workings and …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)