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Challenges of Reinforcement Learning

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Abstract

This chapter introduces the existing challenges in deep reinforcement learning research and applications, including: (1) the sample efficiency problem; (2) stability of training; (3) the catastrophic interference problem; (4) the exploration problems; (5) meta-learning and representation learning for the generality of reinforcement learning methods across tasks; (6) multi-agent reinforcement learning with other agents as part of the environment; (7) sim-to-real transfer for bridging the gaps between simulated environments and the real world; (8) large-scale reinforcement learning with parallel training frameworks to shorten the wall-clock time for training, etc. This chapter proposes the above challenges with potential solutions and research directions, as the primers of the advanced topics in the second main part of the book, including Chaps. 8–12, to provide the readers a relatively comprehensive understanding about the deficiencies of present methods, recent development, and future directions in deep reinforcement learning.

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Notes

  1. 1.

    Figures source: https://gym.openai.com/envs/#atari.

  2. 2.

    https://openai.com/blog/learning-montezumas-revenge-from-a-single-demonstration/.

  3. 3.

    Data source: Oriol Vinyals, Deep Reinforcement Learning Workshop, NeurIPS 2019.

  4. 4.

    Richard S. Sutton. “The Bitter Lesson.” March 13, 2019.

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Authors and Affiliations

  1. Imperial College London, London, UK

    Zihan Ding

  2. Peking University, Beijing, China

    Hao Dong

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  1. Zihan Ding
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  2. Hao Dong
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Correspondence to Zihan Ding .

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Editors and Affiliations

  1. EECS, Peking University, Beijing, China

    Hao Dong

  2. CS, Imperial College London, London, UK

    Zihan Ding

  3. EECS, University of California, Berkeley, Berkeley, USA

    Shanghang Zhang

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Ding, Z., Dong, H. (2020). Challenges of Reinforcement Learning. In: Dong, H., Ding, Z., Zhang, S. (eds) Deep Reinforcement Learning. Springer, Singapore. https://doi.org/10.1007/978-981-15-4095-0_7

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