← Back to papers
2018 IJCAI IJCAI 2018

Exploration by Distributional Reinforcement Learning

Abstract

We propose a framework based on distributional reinforcement learning and recent attempts to combine Bayesian parameter updates with deep reinforcement learning. We show that our proposed framework conceptually unifies multiple previous methods in exploration. We also derive a practical algorithm that achieves efficient exploration on challenging control tasks.

🌉 Interdisciplinary Bridge — Machine Learning and 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
🧭 Keyword Pioneer — bayesian parameter update
🐣 Hot Topic Early Bird — policy evaluation

Authors

Yunhao Tang , Shipra Agrawal

Topics

Artificial Intelligence > Core AI > Agent Systems Machine Learning > Optimization & Theory > Bayesian Inference Reinforcement Learning > Methods > Deep RL Reinforcement Learning > Applications > Value Iteration Machine Learning > Learning Types > Reinforcement Learning

Keywords

deep reinforcement learning policy evaluation bayesian inference distributional reinforcement learning exploration strategy reward distribution bayesian parameter update
Download PDF

Related papers

Semi-Supervised Multi-Modal Learning with Incomplete Modalities 2018
High-dimensional Similarity Learning via Dual-sparse Random Projection 2018
FISH-MML: Fisher-HSIC Multi-View Metric Learning 2018
Generative Warfare Nets: Ensemble via Adversaries and Collaborators 2018
Semi-Supervised Optimal Margin Distribution Machines 2018