Human-like autonomy emerges from self-play and a pinch of human data

arXiv:2606.19370v1 Announce Type: new Abstract: Self-play reinforcement learning has recently emerged as a way to train driving policies without any human data. It uses cheap, large-scale simulations to substitute expensive, large-scale human driving demonstrations. A key limitation of this approach is that policies trained through pure self-play can learn effective but alien driving conventions incompatible with people. Previous works attempt to mitigate such behavioral misalignments through extensive reward engineering and domain randomization, which are brittle and labor-intensive. Instead

Source: arXiv cs.LG — read the full report at the original publisher.

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