Deep Reinforcement Learning

tags
Reinforcement Learning, Deep Learning

Deep reinforcement learning is Reinforcement Learning using deep Neural Networks. It is a field popularized by (Mnih et al. 2015), but using Connectionist Networks in RL has been persued much earlier (Tesauro 1994), (Bakker 2002), (Rummery and Niranjan 1994), (Williams 1992), (Liu and Zou 2017) and many others.

References

Bakker, Bram. 2002. “Reinforcement Learning with Long Short-Term Memory.” In Advances in Neural Information Processing Systems 14, edited by T. G. Dietterich, S. Becker, and Z. Ghahramani. MIT Press.
Liu, Ruishan, and James Zou. 2017. “The Effects of Memory Replay in Reinforcement Learning.” arXiv:1710.06574 [Cs, Stat]. https://arxiv.org/abs/1710.06574.
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, et al. 2015. “Human-Level Control through Deep Reinforcement Learning.” Nature.
Rummery, Gavin A, and Mahesan Niranjan. 1994. On-Line Q-learning Using Connectionist Systems. University of Cambridge, Department of Engineering Cambridge, UK.
Tesauro, Gerald. 1994. “TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play.” Neural Computation.
Williams, Ronald J. 1992. “Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning.” In Reinforcement Learning. The Springer International Series in Engineering and Computer Science. Springer, Boston, MA.

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