Reinforcement Learning
In another follow-up paper SWA was shown to improve the performance of policy gradient methods A2C and DDPG on several Atari games and MuJoCo environments [3]. This application is also an instance of where SWA is used with Adam. Recall that SWA is not specific to SGD and can benefit essentially any optimizer.
{:.table.table-striped.table-bordered}
 | Environment Name | A2C | A2C + SWA | 
| ------------- | ------------- |  ------------- |
| Breakout | 522 ± 34 | 703 ± 60 |
| Qbert | 18777 ± 778 | 21272 ± 655 |
| SpaceInvaders | 7727 ± 1121 | 21676 ± 8897 |
| Seaquest | 1779 ± 4 | 1795 ± 4 |
| BeamRider | 9999 ± 402 | 11321 ± 1065 |
| CrazyClimber | 147030 ± 10239 | 139752 ± 11618 |
Low Precision Training