DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous Driving
Learning motion planning policies that remain reliable under closed-loop execution is a central challenge in Autonomous Driving (AD).
ProofPaper ↗
Key points
- Imitation Learning (IL) scales well to large AD datasets but suffers from distribution shift, causal confusion, and compounding errors under closed-loop execution.
- We study a simple closed-loop IL framework that uses RL purely as an optimization tool, guided by a trajectory-distance reward derived from logged expert data.
- Through a controlled empirical study we find evidence that the training procedure, rather than model size and complexity, accounts for a substantial part of the performance gains.
- We then introduce DriveMLP, a lightweight MLP policy trained with EGRL, as a simple instantiation of these findings.
Sources (1)
- [1]DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous DrivingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:53 PM
Learning motion planning policies that remain reliable under closed-loop execution is a central challenge in Autonomous Driving (AD).
Imitation Learning (IL) scales well to large AD datasets but suffers from distribution shift, causal confusion, and compounding errors under closed-loop execution.
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