← Back to papers
2021 AAAI AAAI 2021

Extending Policy Shaping to Continuous State Spaces (Student Abstract)

Abstract

Abstract Policy Shaping is a Human-in-the-loop Reinforcement Learning (HRL) algorithm. We extend this work to continuous states with our algorithm, Deep Policy Shaping (DPS). DPS uses a feedback neural network that learns the optimality of actions from noisy feedback combined with an RL algorithm. In simulation, we find that DPS outperforms or matches baselines averaged over multiple hyperparameter settings and varying feedback correctness.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Reinforcement Learning
🧭 Keyword Pioneer — human-in-the-loop reinforcement learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Thomas Wei , Taylor A. Kessler Faulkner , Andrea L. Thomaz

Topics

Reinforcement Learning > Methods > Deep RL Reinforcement Learning > Methods > Policy Learning Deep Learning > Learning Types > Reinforcement Learning Artificial Intelligence > Core AI > Reinforcement Learning

Keywords

deep reinforcement learning reinforcement learning human-in-the-loop learning deep learning continuous state space policy shaping human-in-the-loop reinforcement learning feedback neural network
Download PDF

Related papers

Contextual Conditional Reasoning 2021
Attention Beam: An Image Captioning Approach (Student Abstract) 2021
Movie Summarization via Sparse Graph Construction 2021
Text Analysis for Understanding Symptoms of Social Anxiety in Student Veterans 2021
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs 2021