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import torch
from torch.optim import Optimizer
class DM_RMSprop(Optimizer):
"""Implements the form of RMSProp used in DM 2015 Atari paper.
Inspired by https://github.com/spragunr/deep_q_rl/blob/master/deep_q_rl/updates.py"""
def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= momentum:
raise ValueError("Invalid momentum value: {}".format(momentum))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if not 0.0 <= alpha:
raise ValueError("Invalid alpha value: {}".format(alpha))
defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay)
super(DM_RMSprop, self).__init__(params, defaults)
def __setstate__(self, state):
super(DM_RMSprop, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('momentum', 0)
group.setdefault('centered', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
momentum = group['momentum']
sq_momentum = group['alpha']
epsilon = group['eps']
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('RMSprop does not support sparse gradients')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['square_avg'] = torch.zeros_like(p.data)
if momentum > 0:
state['momentum_buffer'] = torch.zeros_like(p.data)
mom_buffer = state['momentum_buffer']
square_avg = state['square_avg']
state['step'] += 1
mom_buffer.mul_(momentum)
mom_buffer.add_((1 - momentum) * grad)
square_avg.mul_(sq_momentum).addcmul_(1 - sq_momentum, grad, grad)
avg = (square_avg - mom_buffer**2 + epsilon).sqrt()
p.data.addcdiv_(-group['lr'], grad, avg)
return loss
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