Vicarious conditioning is proposed as a new intrinsic reward in RL that implements attention, retention, reproduction, and reinforcement via memory methods to enable low-shot learning from others without their policies or rewards, yielding longer episodes in tested environments.
arXiv preprint arXiv:2305.04412 , year =
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MAGNIFIED applies RL fine-tuning to MLLMs for autonomous driving motion planning, yielding over 10.5% lower overlap rate and 38.9% lower off-road rate than SFT baseline on Waymo Open Motion Dataset.
citing papers explorer
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Intrinsic Vicarious Conditioning for Deep Reinforcement Learning
Vicarious conditioning is proposed as a new intrinsic reward in RL that implements attention, retention, reproduction, and reinforcement via memory methods to enable low-shot learning from others without their policies or rewards, yielding longer episodes in tested environments.
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MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning
MAGNIFIED applies RL fine-tuning to MLLMs for autonomous driving motion planning, yielding over 10.5% lower overlap rate and 38.9% lower off-road rate than SFT baseline on Waymo Open Motion Dataset.