Optimizer for PyTorch which could be configured as Adam, AdaMax, AMSGrad or interpolate between them. Like AMSGrad, GAdam maintains maximum value of squared gradient for each parameter, but also GAdam does decay this value over time.
When used with reinforcement learning (Atari + custom PPO implementation) it produces slightly better results than vanilla Adam. Though, I haven't done an extensive hyperparameter search.
With betas = (beta1, 0) and amsgrad_decay = beta2 it will become AdaMax.
With amsgrad_decay = 0 it will become AMSGrad.
I've found it's better to use something in-between, like
betas= (0.9, 0.99) andamsgrad_decay= (0.0001)betas= (0.9, 0.95) andamsgrad_decay= (0.05)
worked best for me, but I've seen good results with wide range of settings.
By default configured as torch.optim.Adam, except late_weight_decay = True as proposed in Fixing Weight Decay Regularization in Adam
# best betas and amsgrad_decay i've found
GAdam(model.parameters(), lr=5e-4, betas=(0.9, 0.99), amsgrad_decay=1e-4)
# works good too
GAdam(model.parameters(), lr=5e-4, betas=(0.9, 0.95), amsgrad_decay=0.05)- optimism (float, optional): Look-ahead factor proposed in Training GANs with Optimism. Must be in [0, 1) range. Value of 0.5 corresponds to paper, 0 disables it, 0.9 is 5x stronger than 0.5 (default: 0)
- avg_sq_mode (str, optional): Specifies how square gradient term should be calculated. Valid values are
- 'weight' will calculate it per-weight as in vanilla Adam (default)
- 'output' will average it over 0 dim of each tensor, i.e. shape[0] average squares will be used for each tensor
- 'tensor' will average it over each tensor
- 'global' will take average of average over each tensor, i.e. only one avg sq value will be used
- l1_decay (float, optional): L1 penalty (default: 0)
- late_weight_decay (boolean, optional): Whether L1 and L2 penalty should be applied before (as proposed in Fixing Weight Decay Regularization in Adam) or after (vanilla Adam) normalization with gradient average squares (default: True)