Speeding Up Reinforcement Learning

Deep reinforcement learning is a powerful tool, but it's held back by its high computational demands. This makes it tough to use in real-world applications. One way to tackle this problem is by reducing the size of the neural networks used in reinforcement learning. However, most current methods for shrinking these networks don't work well with the hardware we use every day.

Researchers have been working on a solution called pruning, which involves cutting out parts of the network that aren't essential. But pruning can be tricky in reinforcement learning because the training process can be unstable. This instability can cause the network to perform poorly after pruning.

A new approach combines two techniques: dynamic structured pruning and model merging. Dynamic structured pruning involves cutting out parts of the network in a way that's compatible with standard hardware. Model merging involves periodically combining multiple versions of the network. This helps to stabilize the training process, even when aggressive pruning is used.

The results are impressive. This new approach can reduce the computational costs of training by up to 72%. At the same time, it maintains performance levels comparable to those of larger networks. It also speeds up the time it takes for the network to make predictions, reducing latency by an average of 16.2%.

This breakthrough has the potential to make reinforcement learning more practical for real-world use. By making it possible to train and deploy reinforcement learning models more efficiently, it could lead to advances in areas like robotics and game playing. The code for this new approach is available online, making it easy for others to build on this work.