Suchergebnisse
Suchergebnisse:
This package is the PyTorch implementation of the Population-coded Spiking Actor Network (PopSAN) that integrates with both on-policy (PPO) and off-policy (DDPG, TD3, SAC) DRL algorithms for learning optimal and energy-efficient continuous control policies.
19. Okt. 2020 · Here, we propose a population-coded spiking actor network (PopSAN) trained in conjunction with a deep critic network using deep reinforcement learning (DRL). The population coding scheme dramatically increased the representation capacity of the network and the hybrid learning combined the training advantages of deep networks with the ...
- Guangzhi Tang, Neelesh Kumar, Raymond Yoo, Konstantinos P. Michmizos
- arXiv:2010.09635 [cs.NE]
- 2020
PopMan is a POP3 and IMAP4 manager. It can be used to list all e-mails received on your incoming mail server, without having to load these mails completely. Unwanted e-mails can be deleted directly from the server. You can use PopMan to check your e-mail accounts in the background. When new e-mails arrive, you will be notified.
Our Loihi-run PopSAN consumed 140 times less energy per inference when compared against the deep actor network on Jetson TX2, and achieved the same level of performance. Our results demonstrate the overall efficiency of neuromorphic controllers and suggest the hybrid reinforcement learning approach as an alternative to deep learning, when both ...
- Guangzhi Tang, Neelesh Kumar, Raymond Yoo, Konstantinos P. Michmizos
- 2020
19. Okt. 2020 · Here, we propose a population-coded spiking actor network (PopSAN) trained in conjunction with a deep critic network using deep reinforcement learning (DRL). The population coding scheme ...
19. Okt. 2020 · Here, we propose a population-coded spiking actor network (PopSAN) trained in conjunction with a deep critic network using deep reinforcement learning (DRL). The population coding scheme dramatically increased the representation capacity of the network and the hybrid learning combined the training advantages of deep networks with the ...
For input coding, we apply population coding with dynamically receptive elds to directly encode each input state component. For neuronal coding, we propose di erent types of dynamic-neurons (containing 1st-order and 2nd-order neuronal dynamics) to describe much more complex neuronal dynamics.
