Diving deeper into distributed reinforcement learning

As we already mentioned in the earlier chapters, training sophisticated reinforcement learning agents requires massive amounts of data. While one critical area of research is to increase the sample efficiency in RL, the other and complementary direction is about how to best utilize the compute power and parallelization and reduce the wall-clock time and cost of training. We already covered, implemented, and used distributed RL algorithms and libraries in the earlier chapters. So, this section will be an extension of the previous discussions due to the importance of this topic. Here, we present additional material on state-of-the-art distributed RL architectures, algorithms, and libraries. With that, let's get started with SEED RL, an architecture designed for massive and efficient parallelization.

Scalable, efficient deep reinforcement learning: SEED RL

Let's first begin the discussion by revisiting the Ape-X architecture...