Summary of Cale: Continuous Arcade Learning Environment, by Jesse Farebrother et al.
CALE: Continuous Arcade Learning Environment
by Jesse Farebrother, Pablo Samuel Castro
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The Continuous Arcade Learning Environment (CALE) extends the Arcade Learning Environment (ALE) to support continuous actions, enabling the evaluation of various agents on Atari 2600 games. This enables benchmarking and research directions for value-based agents like DQN and Rainbow, as well as continuous-control agents like PPO and SAC. Initial results using Soft Actor-Critic are provided. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The CALE lets computers play classic video games better. It’s a special tool that helps scientists test and compare different kinds of computer programs (called “agents”) that can control characters in the games. These agents use artificial intelligence to make decisions, like when to jump or shoot. The CALE makes it easier for researchers to try out new ideas and see how well they work. |
