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With the Deep Neural Networks (DNNs) as a powerful function approximator, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks.
SpiNNaker: A 1-W 18-core system-on-chip for massively-parallel neural network simulation
Painkras, E.; Plana, L. A.; Garside, J.; Temple, S.; Galluppi, F.; Patterson, C.; Lester, D. R.; Brown, A. D.; and Furber, S. B. 2013 · 1953
Earlier work this paper cites.
Neuronal population coding of movement direction
Georgopoulos, A. P.; Schwartz, A. B.; and Kettner, R. E. 1986 · 1986
Earlier work this paper cites.
Introduction to theoretical neurobiology: volume 2, nonlinear and stochastic theories
Tuckwell, H. C. 1988 · 1988
Earlier work this paper cites.
Reinforcement learning: A survey
Kaelbling, L. P.; Littman, M. L.; and Moore, A. W. 1996 · 1996
Earlier work this paper cites.
Networks of spiking neurons: the third generation of neural network models
Maass, W. 1997 · 1997
Earlier work this paper cites.
Reinforcement learning in continuous time and space
Doya, K. 2000 · 2000
Earlier work this paper cites.
Spiking neuron models: Single neurons, populations, plasticity
Gerstner, W.; and Kistler, W. M. 2002 · 2002
Earlier work this paper cites.
Organization of cell assemblies in the hippocampus
Harris, K. D.; Csicsvari, J.; Hirase, H.; Dragoi, G.; and Buzsaki, G. 2003 · 2003
Earlier work this paper cites.
Simple model of spiking neurons
Izhikevich, E. M. 2003 · 2003
Earlier work this paper cites.
Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity
Florian, R. V. 2007 · 2007
Earlier work this paper cites.
Rathi, N.; and Roy, K. 2020 · 2008
Earlier work this paper cites.
Finite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning
Cheng, X.; Zhang, T.; Jia, S.; and Xu, B. 2020b · 2010
Earlier work this paper cites.
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
Tang, G.; Kumar, N.; Yoo, R.; and Michmizos, K. P. 2020 · 2010
Earlier work this paper cites.
Reinforcement learning using a continuous time actor-critic framework with spiking neurons
Frémaux, N.; Sprekeler, H.; and Gerstner, W. 2013 · 2013
Cited alongside, same era.
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O’Brien, M. J.; and Srinivasa, N. 2013 · 2013
Cited alongside, same era.
Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; et al. 2015 · 2015
Cited alongside, same era.
Brockman, G.; Cheung, V.; Pettersson, L.; Schneider, J.; Schulman, J.; Tang, J.; and Zaremba, W. 2016 · 2016
Cited alongside, same era.
Benchmarking deep reinforcement learning for continuous control
Duan, Y.; Chen, X.; Houthooft, R.; Schulman, J.; and Abbeel, P. 2016 · 2016
Cited alongside, same era.
Superspike: Supervised learning in multilayer spiking neural networks
Zenke, F.; and Ganguli, S. 2018 · 2018
Later among the works it cites.
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Zhang, Tielin and Zeng, Yi and Shi, Mengting and Zhao, Dongcheng. 2018 · 2018
Later among the works it cites.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game
Patel, D.; Hazan, H.; Saunders, D. J.; Siegelmann, H. T.; and Kozma, R. 2019 · 2019
Later among the works it cites.
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Rathi, N.; Srinivasan, G.; Panda, P.; and Roy, K. 2019 · 2019
Later among the works it cites.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O.; Babuschkin, I.; Czarnecki, W. M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D. H.; Powell, R.; Ewalds, T.; Georgiev, P.; et al. 2019 · 2019
Later among the works it cites.
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Lillicrap, T. P.; Hunt, J. J.; Pritzel, A.; Heess, N.; Erez, T.; Tassa, Y.; Silver, D.; and Wierstra, D. 2016 · 2016
Cited alongside, same era.
HMSNN: Hippocampus inspired Memory Spiking Neural Network
Zhang, T.; Zeng, Y.; Zhao, D.; Wang, L.; Zhao, Y.; and Xu, B. 2016 · 2016
Cited alongside, same era.
Improving multi-layer spiking neural networks by incorporating brain-inspired rules
Zeng, Y.; Zhang, T.; and Xu, B. 2017 · 2017
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
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Cited alongside, same era.
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Henderson, P.; Islam, R.; Bachman, P.; Pineau, J.; Precup, D.; and Meger, D. 2018 · 2018
Cited alongside, same era.
Spiking neural network reinforcement learning method based on temporal coding and STDP
Sboev, A.; Vlasov, D.; Rybka, R.; and Serenko, A. 2018 · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Sutton, R. S.; and Barto, A. G. 2018 · 2018
Cited alongside, same era.
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Yuan, M.; Wu, X.; Yan, R.; and Tang, H. 2019 · 2019
Later among the works it cites.
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Zou, L.; Xia, L.; Ding, Z.; Song, J.; Liu, W.; and Yin, D. 2019 · 2019
Later among the works it cites.
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Comsa, I. M.; Potempa, K.; Versari, L.; Fischbacher, T.; Gesmundo, A.; and Alakuijala, J. 2020 · 2020
Later among the works it cites.
A Curiosity-Based Learning Method for Spiking Neural Networks
Shi, M.; Zhang, T.; and Zeng, Y. 2020 · 2020
Later among the works it cites.
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Tang, G.; Kumar, N.; and Michmizos, K. P. 2020 · 2020
Later among the works it cites.
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GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning
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