README.md

Weight-Clipping

The official repo for reproducing the experiments and weight clipping implementation. You can find the paper from this link. Here we give a minimal implementation for weight clipping with SGD (change torch.optim.SGD to torch.optim.Adam` if you want to use Adam).

import torch, math

class InitBounds:
    def __init__(self):
        self.previous_weight = None
    def get(self, p):
        if p.dim() == 1:
            fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.previous_weight)
            return 1.0 / math.sqrt(fan_in)
        elif p.dim() == 2 or p.dim() == 4:
            self.previous_weight = p
            fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(p)
            return  1.0 / math.sqrt(fan_in)
        else:
            raise ValueError("Unsupported tensor dimension: {}".format(p.dim()))

class WeightClippingSGD(torch.optim.Optimizer):
    def __init__(self, params, kappa=1.0, optimizer=torch.optim.SGD, **kwargs):
        defaults = dict(kappa=kappa)
        super(WeightClippingSGD, self).__init__(params, defaults)
        self.optimizer = optimizer(self.param_groups, **kwargs)
        self.param_groups = self.optimizer.param_groups
        self.defaults.update(self.optimizer.defaults)
        self.init_bounds = InitBounds()
    def step(self, closure=None):
        self.zero_grad()
        loss = closure()
        loss.backward()
        self.optimizer.step()
        self.weight_clipping()
    def weight_clipping(self):
        for group in self.param_groups:
            for p in group["params"]:
                bound = self.init_bounds.get(p)
                p.data.clamp_(-group["kappa"] * bound, group["kappa"] * bound)

Reproducing results:

1. You need to have environemnt with python 3.11:

conda create --name torch python==3.11
conda activate torch

2. Install Dependencies:

python -m pip install --upgrade pip
pip install .

3. TBD

License

Distributed under the MIT License. See LICENSE for more information.

How to cite

Bibtex:

@article{elsayed2024weight,
    title={Weight Clipping for Deep Continual and Reinforcement Learning},
    author={Elsayed, Mohamed and Lan, Qingfeng and Lyle, Clare and Mahmood, A. Rupam},
    journal={Reinforcement Learning Journal},
    volume={5},
    pages={2198--2217},
    year={2024}
}