Deep Learning with Reinforcement Learning – ICLR 2017 Discoveries

The 5th International Conference on Learning Representation (ICLR 2017) is coming to Toulon, France (April 24-26 2017).

This blog post gives an overview of Deep Learning with Reinforcement related papers submitted to ICLR 2017, see underneath for the list of papers. If you want to learn more about combining Deep Learning with Reinforcement Learning check out Nervana’s Demystifying Deep Reinforcement Learning, Andrej Karpathy’s Deep Reinforcement Learning: Pong From Pixels, DeepMind’s Deep Reinforcement Learning, and Berkeley University’s CS 294: Deep Reinforcement Learning (starting in Sprint 2017)

Best regards,

Amund Tveit

ICLR 2017 – Reinforcement Related paper

  1. Stochastic Neural Networks for Hierarchical Reinforcement Learning – Authors: Carlos Florensa, Yan Duan, Pieter Abbeel
  2. #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning – Authors: Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
  3. Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning – Authors: Abhishek Gupta, Coline Devin, YuXuan Liu, Pieter Abbeel, Sergey Levine
  4. Deep Reinforcement Learning for Accelerating the Convergence Rate – Authors: Jie Fu, Zichuan Lin, Danlu Chen, Ritchie Ng, Miao Liu, Nicholas Leonard, Jiashi Feng, Tat-Seng Chua
  5. Generalizing Skills with Semi-Supervised Reinforcement Learning – Authors: Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine
  6. Learning to Perform Physics Experiments via Deep Reinforcement Learning – Authors: Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter Battaglia, Nando de Freitas
  7. Designing Neural Network Architectures using Reinforcement Learning – Authors: Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar
  8. Reinforcement Learning with Unsupervised Auxiliary Tasks -Authors: Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, Koray Kavukcuoglu
  9. Options Discovery with Budgeted Reinforcement Learning – Authors: Aurelia Lon, Ludovic Denoyer
  10. Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU – Authors: Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons,Jan Kautz
  11. Multi-task learning with deep model based reinforcement learning – Authors: Asier Mujika
  12. Neural Architecture Search with Reinforcement Learning -Authors: Barret Zoph, Quoc Le
  13. Tuning Recurrent Neural Networks with Reinforcement Learning -Authors: Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck
  14. RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning – Authors: Yan Duan, John Schulman, Xi Chen, Peter Bartlett, Ilya Sutskever, Pieter Abbeel
  15. Learning to Repeat: Fine Grained Action Repetition
    for Deep Reinforcement Learning
    – Authors: Sahil Sharma, Aravind S. Lakshminarayanan, Balaraman Ravi
    ndran
  16. Learning to Play in a Day: Faster Deep Reinforcemen
    t Learning by Optimality Tightening
    – Authors: Frank S.He, Yang Liu, Alexander G. Schwing, Jian Peng
  17. Surprise-Based Intrinsic Motivation for Deep Reinfo
    rcement Learning
    – Authors: Joshua Achiam, Shankar Sastry
  18. Learning to Compose Words into Sentences with Reinf
    orcement Learning
    – Authors: Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
  19. Spatio-Temporal Abstractions in Reinforcement Learn
    ing Through Neural Encoding
    – Authors: Nir Baram, Tom Zahavy, Shie Mannor
  20. Modular Multitask Reinforcement Learning with Policy Sketches – Authors: Jacob Andreas, Dan Klein, Sergey Levine
  21. Combating Deep Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear – Authors: Zachary C. Lipton, Jianfeng Gao, Lihong Li, Jianshu Chen, Li Deng

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