Fetching the paper…
Reading the bibliography…
The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by limited on-board resources.
Neuronal population coding of movement direction
A. P. Georgopoulos, A. B. Schwartz, and R. E. Kettner · 1986
Earlier work this paper cites.
What can a neuron learn with spike-timing-dependent plasticity?
R. Legenstein, C. Naeger, and W. Maass · 2005
Earlier work this paper cites.
Neural correlations, population coding and computation
B. B. Averbeck, P. E. Latham, and A. Pouget · 2006
Earlier work this paper cites.
Optimal population coding by noisy spiking neurons
G. Tkačik, J. S. Prentice, V. Balasubramanian, and E. Schneidman · 2010
Earlier work this paper cites.
Reinforcement learning using a continuous time actor-critic framework with spiking neurons
N. Frémaux, H. Sprekeler, and W. Gerstner · 2013
Earlier work this paper cites.
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Earlier work this paper cites.
Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Earlier work this paper cites.
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi · 2017
Earlier work this paper cites.
More is less: A more complicated network with less inference complexity
X. Dong, J. Huang, Y. Yang, and S. Yan · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Earlier work this paper cites.
Learning end-to-end multimodal sensor policies for autonomous navigation
G.-H. Liu, A. Siravuru, S. Prabhakar, M. Veloso, and G. Kantor · 2017
Earlier work this paper cites.
Resparc: A reconfigurable and energy-efficient architecture with memristive crossbars for deep spiking neural networks
A. Ankit, A. Sengupta, P. Panda, and K. Roy · 2017
Cited alongside, same era.
Automated deep reinforcement learning environment for hardware of a modular legged robot
S. Ha, J. Kim, and K. Yamane · 2018
Cited alongside, same era.
Loihi: A neuromorphic manycore processor with on-chip learning
M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y. Cao, S. H. Choday, G. Dimou, P. Joshi, N. Imam, S. Jain, et al · 2018
Cited alongside, same era.
End to end learning of spiking neural network based on r-stdp for a lane keeping vehicle
Z. Bing, C. Meschede, K. Huang, G. Chen, F. Rohrbein, M. Akl, and A. Knoll · 2018
Cited alongside, same era.
Long short-term memory and learning-to-learn in networks of spiking neurons
G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass · 2018
Cited alongside, same era.
Learning first-to-spike policies for neuromorphic control using policy gradients
B. Rosenfeld, O. Simeone, and B. Rajendran · 2019
Later among the works it cites.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari breakout game
D. Patel, H. Hazan, D. J. Saunders, H. T. Siegelmann, and R. Kozma · 2019
Later among the works it cites.
Neural population coding for effective temporal classification
Z. Pan, J. Wu, M. Zhang, H. Li, and Y. Chua · 2019
Later among the works it cites.
Challenges of real-world reinforcement learning
G. Dulac-Arnold, D. Mankowitz, and T. Hester · 2019
Later among the works it cites.
Event-driven visual-tactile sensing and learning for robots
T. Taunyazoz, W. Sng, H. H. See, B. Lim, J. Kuan, A. F. Ansari, B. Tee, and H. Soh · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep reinforcement learning that matters
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2018
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
S. Fujimoto, H. Hoof, and D. Meger · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
Cited alongside, same era.
Spatio-temporal backpropagation for training high-performance spiking neural networks
Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi · 2018
Cited alongside, same era.
Event-based vision meets deep learning on steering prediction for self-driving cars
A. I. Maqueda, A. Loquercio, G. Gallego, N. García, and D. Scaramuzza · 2018
Cited alongside, same era.
Spiking neural network on neuromorphic hardware for energy-efficient unidimensional slam
G. Tang, A. Shah, and K. P. Michmizos · 2019
Cited alongside, same era.
C. Michaelis, A. B. Lehr, and C. Tetzlaff · 2020
Closest in time.
N. Rathi, G. Srinivasan, P. Panda, and K. Roy · 2020
Closest in time.
Reinforcement co-learning of deep and spiking neural networks for energy-efficient mapless navigation with neuromorphic hardware
G. Tang, N. Kumar, and K. P. Michmizos · 2020
Closest in time.
A spiking neural network emulating the structure of the oculomotor system requires no learning to control a biomimetic robotic head
P. Balachandar and K. P. Michmizos · 2020
Closest in time.
An on-chip spiking neural network for estimation of the head pose of the icub robot
R. Kreiser, A. Renner, V. R. Leite, B. Serhan, C. Bartolozzi, A. Glover, and Y. Sandamirskaya · 2020
Closest in time.
Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
B. Han, G. Srinivasan, and K. Roy · 2020
Closest in time.