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Distributional Reinforcement Learning for Efficient Exploration - 2019

Research Area:  Machine Learning


In distributional reinforcement learning (RL), the estimated distribution of value functions model both the parametric and intrinsic uncertainties. We propose a novel and efficient exploration method for deep RL that has two components. The first is a decaying schedule to suppress the intrinsic uncertainty. The second is an exploration bonus calculated from the upper quantiles of the learned distribution. In Atari 2600 games, our method achieves 483 % average gain across 49 games in cumulative rewards over QR-DQN. We also compared our algorithm with QR-DQN in a challenging 3D driving simulator (CARLA). Results show that our algorithm achieves nearoptimal safety rewards twice faster than QRDQN.

Author(s) Name:  Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

Journal name:  

Conferrence name:  Proceedings of the 36th International Conference on Machine Learning

Publisher name:  arxiv

DOI:  10.48550/arXiv.1905.06125

Volume Information: