Out of the Box

사용자 도구

사이트 도구

추적: • tiny_llm • 2024-01_asynchronous_local-sgd_training_for_language_modeling
drl:start

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양쪽 이전 판이전 판
다음 판		이전 판
drl:start [2016/11/30 06:16] – [Exploration 개선] rex8312drl:start [2024/03/23 02:42] (현재) – 바깥 편집 127.0.0.1
줄 1: 줄 1:
-===== DRL 관련연구 목록 =====+===== General Video Game Playing =====
  
 +==== Learning Algorithm ====
 +
 +  * [[https://arxiv.org/abs/1704.04651|The Reactor: A Sample-Efficient Actor-Critic Architecture]]
 +
 +==== Reward Shaping ====
 +
 +  * [[https://arxiv.org/abs/1611.05397|Reinforcement Learning with Unsupervised Auxiliary Tasks]]
 +
 +==== Simulation ====
 +
 +  * [[https://arxiv.org/abs/1704.02254|RECURRENT ENVIRONMENT SIMULATORS]]
 +  * [[https://arxiv.org/abs/1507.08750|Action-Conditional Video Prediction using Deep Networks in Atari Games]]
 +  * 
 +
 +==== Learning by Instruction ====
 +
 +  * [[https://arxiv.org/abs/1704.05539|Beating Atari with Natural Language Guided Reinforcement Learning]]
 +==== ?? =====
 +
 +  * ??
 +    * Z. C. Lipton, J. Gao, L. Li, X. Li, F. Ahmed, and L. Deng, “Efficient Exploration for Dialogue Policy Learning with BBQ Networks & Replay Buffer Spiking,” arXiv:1608.05081 [cs, stat], Aug. 2016.
   * Adaptive Normalization   * Adaptive Normalization
     * H. van Hasselt et al., “Learning values across many orders of magnitude,” arXiv:1602.07714 [cs, stat], Feb. 2016.     * H. van Hasselt et al., “Learning values across many orders of magnitude,” arXiv:1602.07714 [cs, stat], Feb. 2016.
줄 15: 줄 36:
   * Progressive Network   * Progressive Network
     * A. A. Rusu et al., “Progressive neural networks,” arXiv preprint arXiv:1606.04671, 2016.     * A. A. Rusu et al., “Progressive neural networks,” arXiv preprint arXiv:1606.04671, 2016.
 +  * [[https://arxiv.org/pdf/1310.8499.pdf|2014-05, Deep AutoRegressive Networks]]
  
-==== 학습 알고리즘 ====+ 
 +==== Deep Reinforcement Learning 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. | ^Deep Q-Learning |V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015. |
줄 33: 줄 56:
 ^ | | ^ | |
  
-==== 인공신경망 아키텍처 ====+==== Architecture ====
  
 ^Dueling Network | Z. Wang, N. de Freitas, and M. Lanctot, “Dueling network architectures for deep reinforcement learning,” arXiv preprint arXiv:1511.06581, 2015.| ^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 ==== 
  
-^ | | +==== AI Platform ====
-^ | |+
  
-==== 응용 ====+^ViZDoom |M. Kempka, M. Wydmuch, G. Runc, J. Toczek, and W. Jaśkowski, “ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning,” arXiv:1605.02097 [cs], May 2016. | 
 +^OpenAIGym|https://gym.openai.com/ | 
 +^Universe|https://universe.openai.com/| 
 +^DeepMind Lab|https://deepmind.com/blog/open-sourcing-deepmind-lab/| 
 +^Malmo|https://github.com/Microsoft/malmo| 
 + 
 +==== Application ====
  
 ^AlphaGo|D. 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.| ^AlphaGo|D. 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.|
-^ | |+^Anold |G. Lample and D. S. Chaplot, “Playing FPS Games with Deep Reinforcement Learning,” arXiv:1609.05521 [cs], Sep. 2016. | 
 + 
 +===== 구현체 =====
  
-{{tag>DRL deep-learning reinforcement-learning}}+  * https://github.com/google/dopamine
      
  
  
  
drl/start.1480486608.txt.gz · 마지막으로 수정됨: (바깥 편집)