How does distributed Q-Learning improve scalability?

Distributed Q-learning parallelizes experience collection and training to improve scalability and sample throughput.

Multiple actors collect experiences in parallel environments and send them to a central learner or shared buffer. This increases diversity of experience and accelerates learning. However, it introduces challenges like stale gradients, synchronization overhead, and non-iid data distribution.