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Found 6 papers, 1 papers with code
Temporally Layered Architecture for Efficient Continuous Control
We present a temporally layered architecture (TLA) for temporally adaptive control with minimal energy expenditure.
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control
Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands.
QuickNets: Saving Training and Preventing Overconfidence in Early-Exit Neural Architectures
However, early exits increase the training time of the neural networks.
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks
The present work focuses on using SNNs in combination with deep reinforcement learning in ATARI games, which involves additional complexity as compared to image classification.
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games
Previous studies in image classification domain demonstrated that standard NNs (with ReLU nonlinearity) trained using supervised learning can be converted to SNNs with negligible deterioration in performance.
