Name
Abstract | ||
|---|---|---|
We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks. Our method can take both advantages of gradient-free optimization and gradient-based optimization methods, of which the former is effective for problems with multiple local minima, while the latter can quickly find local minima. In addition, multiple chromosomes can define different models, making it easy to construct multiple models and can be effectively applied to problems that require multiple models. We show that this G2N can be applied to typical reinforcement learning algorithms to achieve a large improvement in sample efficiency and performance. |
Year | Venue | Keywords |
|---|---|---|
2018 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | neural networks,local minima,deep reinforcement learning,multiple models |
Field | DocType | Volume |
Computer science,Maxima and minima,Artificial intelligence,Artificial neural network,Machine learning,Binary number,Multiple Models,Reinforcement learning | Conference | 31 |
ISSN | Citations | PageRank |
1049-5258 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simyung Chang | 1 | 2 | 6.15 |
| John Yang | 2 | 1 | 3.06 |
| Jaeseok Choi | 3 | 0 | 1.35 |
| Nojun Kwak | 4 | 862 | 63.79 |