Out of the Box

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문서의 이전 판입니다!

목차

DRL 관련연구 목록

  • Adaptive Normalization
    • H. van Hasselt et al., “Learning values across many orders of magnitude,” arXiv:1602.07714 [cs, stat], Feb. 2016.
  • Safe Off-Policy
    • R. Munos et al., “Safe and Efficient Off-Policy Reinforcement Learning,” arXiv preprint arXiv:1606.02647, 2016.
  • Empowerment
    • S. Mohamed and D. J. Rezende, “Variational information maximisation for intrinsically motivated reinforcement learning,” Advances in Neural Information Processing Systems, pp. 2125–2133, 2015.
  • Universal Values
    • T. Schaul et al., “Universal value function approximators,” in Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015, pp. 1312–1320.
  • Macro-Actions
    • A. Vezhnevets et al., “Strategic Attentive Writer for Learning Macro-Actions,” in Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 3486–3494.
  • Successor Features
    • A. Barreto et al., “Successor Features for Transfer in Reinforcement Learning,” arXiv preprint arXiv:1606.05312, 2016.
  • Progressive Network
    • A. A. Rusu et al., “Progressive neural networks,” arXiv preprint arXiv:1606.04671, 2016.

학습 알고리즘

Deep Q-Learning V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015.
Double DQN H. van Hasselt, A. Guez, and D. Silver, “Deep Reinforcement Learning with Double Q-learning,” arXiv:1509.06461 [cs], Sep. 2015.
DDPGT. P. Lillicrap et al., “Continuous control with deep reinforcement learning,” arXiv:1509.02971 [cs, stat], Sep. 2015.
Async. Deep RL V. Mnih et al., “Asynchronous Methods for Deep Reinforcement Learning,” arXiv:1602.01783 [cs], Feb. 2016.

Exploration 개선

Count & Exploration I. Osband, C. Blundell, A. Pritzel, and B. Van Roy, “Deep Exploration via Bootstrapped DQN,” arXiv:1602.04621 [cs, stat], Feb. 2016.

Replay memory 개선

Prioritized Replay T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized Experience Replay,” arXiv:1511.05952 [cs], Nov. 2015.

인공신경망 아키텍처

Dueling Network Z. Wang, N. de Freitas, and M. Lanctot, “Dueling network architectures for deep reinforcement learning,” arXiv preprint arXiv:1511.06581, 2015.

Neural Differential Machine

플랫폼

응용

AlphaGoD. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, Jan. 2016.
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drl/start.1480486608.txt.gz · 마지막으로 수정됨: 2024/03/23 02:38 (바깥 편집)