Summary of Closed-loop Supervised Fine-tuning Of Tokenized Traffic Models, by Zhejun Zhang et al.
Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
by Zhejun Zhang, Peter Karkus, Maximilian Igl, Wenhao Ding, Yuxiao Chen, Boris Ivanovic, Marco Pavone
First submitted to arxiv on: 5 Dec 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 The paper presents a solution to mitigate covariate shift in traffic simulation by introducing Closest Among Top-K (CAT-K) rollouts, a closed-loop fine-tuning strategy. The approach is inspired by large language models and tokenized multi-agent policies, which have become the state-of-the-art in traffic simulation. However, these methods typically suffer from covariate shift when executed in closed-loop during simulation. CAT-K fine-tuning uses existing trajectory data without reinforcement learning or generative adversarial imitation. The results show that a small 7M-parameter tokenized traffic simulation policy outperforms a larger 102M-parameter model, achieving the top spot on the Waymo Sim Agent Challenge leaderboard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in traffic simulation by creating a new way to make traffic simulators better. The idea is inspired by how language models work and uses something called tokenized multi-agent policies. These policies are usually trained one way but have a big problem when used in real situations. To fix this, the authors created a simple method that uses existing data without needing to learn or train new models. This helps make simulators better and even beats bigger models on a challenge leaderboard. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning
