Oxford University researchers present Craftax: a machine learning benchmark for open reinforcement learning

The creation and use of appropriate benchmarks is an important driver of the advancement of RL algorithms. For deep value-based RL algorithms, there is the Arcade learning environment; for continuous control, there is Mujoco; and for multi-agent RL, there's the StarCraft Multi-Agent Challenge. As part of the movement toward more generic agents, benchmarks have emerged that demonstrate more open dynamics, such as procedural world generation, skill acquisition and reuse, long-term dependencies, and constant learning. Because of this, tools like MiniHack, Crafter, MALMO and The NetHack Learning Environment have been created.

Unfortunately, researchers cannot use them due to their long runtime, making them impractical for use with current methods that do not employ large-scale computing resources. At the same time, JAX has seen a boom in RL environments as the speed of execution of an end-to-end compiled RL pipeline has been fully realized. Experiments that used to take days to run on a huge compute cluster can now be completed in minutes on a single GPU thanks to effective parallelization and compilation and the elimination of CPU-GPU handoff.

To bring these two schools of thought together, a recent study by the University of Oxford and University College London provides the Craftax benchmark, a JAX-based environment that runs orders of magnitude faster than similar ones and exhibits intricate dynamics. and open. A concrete example is Craftax-Classic, a JAX reimplementation of Crafter that outperforms the original Python version by 250.

The researchers show that a basic PPO agent can solve Craftax-Classic (up to 90% of peak performance) in 51 minutes with easy access to many more time steps. Consequently, they also offer Craftax, a much more difficult scenario that borrows mechanics from NetHack and, in general, the Roguelike genre. They provide users with the core Craftax environment, designed to be more difficult while maintaining a fast runtime, to offer a more engaging challenge. A wide variety of new game mechanics are introduced in Craftax. Using pixels simply adds another layer of representation learning to the problem, and many of the qualities Crafter examines (exploration, memory) are not concerned with the precise form of the observation. Therefore, they provide variants of Craftax that use symbolic observations as well as pixel-based observations; the first one is about ten times faster.

Their test results reveal that currently available approaches perform poorly on Craftax. Therefore, the team hopes that it will enable experimentation with limited computational resources while posing a substantial challenge for future RL research.

The team hopes that Craftax-Classic will offer a smooth introduction to Craftax for people who are already familiar with the Crafter standard.

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