Summary of A Benchmark Environment For Offline Reinforcement Learning in Racing Games, by Girolamo Macaluso et al.
A Benchmark Environment for Offline Reinforcement Learning in Racing Games
by Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 introduce OfflineMania, a novel environment for offline reinforcement learning (ORL) research, inspired by the TrackMania series. The goal is to develop more efficient algorithms that can learn from pre-collected datasets without requiring continuous environmental interactions. This approach has the potential to reduce training time and increase efficiency in applications such as AAA games. The authors provide various datasets to test ORL performance, including policies of varying ability and sizes, serving as a challenging testbed for algorithm development and evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OfflineMania is an environment that simulates a racing game where the goal is to complete the track through optimal navigation. It’s created using the Unity 3D game engine and inspired by the TrackMania series. This means we can learn from a dataset of pre-collected transitions, which helps us develop more efficient algorithms. The researchers also provide baselines for different types of reinforcement learning approaches. |
Keywords
* Artificial intelligence * Reinforcement learning
