Name
Abstract | ||
|---|---|---|
Neuroevolution has been used to train Deep Neural Networks on reinforcement learning problems. A few attempts have been made to extend it to address either multi-task or multi-objective optimization problems. This research work presents the Multi-Task Multi-Objective Deep Neuroevolution method, a highly parallelizable algorithm that can be adopted for tackling both multi-task and multi-objective problems. In this method prior knowledge on the tasks is used to explicitly define multiple utility functions, which are optimized simultaneously. Experimental results on some Atari 2600 games, a challenging testbed for deep reinforcement learning algorithms, show that a single neural network with a single set of parameters can outperform previous state of the art techniques. In addition to the standard analysis, all results are also evaluated using the Hypervolume indicator and the Kullback-Leibler divergence to get better insights on the underlying training dynamics. The experimental results show that a neural network trained with the proposed evolution strategy can outperform networks individually trained respectively on each of the tasks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-61616-8_11 | ICANN (2) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvatore Danilo Riccio | 1 | 0 | 0.34 |
| Deyan Dyankov | 2 | 0 | 0.34 |
| Giorgio Jansen | 3 | 0 | 0.34 |
| Giuseppe Di Fatta | 4 | 529 | 39.23 |
| Giuseppe Nicosia | 5 | 0 | 1.69 |