An Information-Theoretic Optimality Principle for Deep Reinforcement Learning
Abstract
We methodologically address the problem of Q-value overestimation in deep reinforcement learning to handle high-dimensional state spaces efficiently. By adapting concepts from information theory, we introduce an intrinsic penalty signal encouraging reduced Q-value estimates. The resultant algorithm encompasses a wide range of learning outcomes containing deep Q-networks as a special case. Different learning outcomes can be demonstrated by tuning a Lagrange multiplier accordingly. We furthermore propose a novel scheduling scheme for this Lagrange multiplier to ensure efficient and robust learning. In experiments on Atari, our algorithm outperforms other algorithms (e.g. deep and double deep Q-networks) in terms of both game-play performance and sample complexity. These results remain valid under the recently proposed dueling architecture.
Cite
@article{arxiv.1708.01867,
title = {An Information-Theoretic Optimality Principle for Deep Reinforcement Learning},
author = {Felix Leibfried and Jordi Grau-Moya and Haitham Bou-Ammar},
journal= {arXiv preprint arXiv:1708.01867},
year = {2018}
}
Comments
Presented at the NIPS Deep Reinforcement Learning Workshop, Montreal, Canada, 2018