Weight Clipping for Deep Continual and Reinforcement Learning

Understand

Many failures in deep continual and reinforcement learning are associated with increasing magnitudes of the weights, making them hard to change and potentially causing overfitting.

• While many methods address these learning failures, they often change the optimizer or the architecture, a complexity that hinders widespread adoption in various systems.
• In this paper, we focus on learning failures that are associated with increasing weight norm and we propose a simple technique that can be easily added on top of existing learning systems: clipping neural network weights to limit them to a specific range.
• We study the effectiveness of weight clipping in a series of supervised and reinforcement learning experiments.