Part 8/11:
While the strides in developing more nuanced training environments are promising, computational constraints remain a significant hurdle. Prior to recent advancements, the interplay of CPUs and GPUs confined the speed at which reinforcement learning could scale, resulting in arduous training times. However, by placing both the training environments and the AI agents on the GPU, a notable acceleration was achieved, significantly increasing the efficiency of simulations and allowing for the evaluation of diverse tasks on a much larger scale.