Reinforcement learning achieves state-of-the-art results when models have access to a vast quantity of state, action, and reward tuples. Because generating this data in the real world is often slow and expensive, highly parallelized environment simulators are used to rapidly generate massive amounts of interaction data, leading to superhuman performance in domains like strategy games and physics.