Summary of Craftax: a Lightning-fast Benchmark For Open-ended Reinforcement Learning, by Michael Matthews and Michael Beukman and Benjamin Ellis and Mikayel Samvelyan and Matthew Jackson and Samuel Coward and Jakob Foerster
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
by Michael Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Jackson, Samuel Coward, Jakob Foerster
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the issue of slow and simple benchmarks in reinforcement learning (RL) by introducing a new framework called Craftax. The authors identify existing benchmarks as either too computationally demanding or not challenging enough for meaningful research. To address this, they create Craftax-Classic, a rewritten version of Crafter that runs up to 250x faster using JAX. This allows researchers to train algorithms like PPO in under an hour on a single GPU while achieving 90% of the optimal reward. The main Craftax benchmark is a more complex extension of Crafter mechanics with elements inspired by NetHack, requiring deep exploration, long-term planning, and memory. Existing methods fail to make significant progress on this benchmark, highlighting the need for new approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning (RL) algorithms are crucial in developing artificial intelligence. A major problem is that current benchmarks are either too slow or not challenging enough for researchers. The authors of this paper create a new benchmark called Craftax to solve this issue. Craftax-Classic, a rewritten version of Crafter, makes it possible to train algorithms quickly and efficiently on regular computers. This allows scientists to develop better AI systems. |
Keywords
* Artificial intelligence * Reinforcement learning
