AI RESEARCH
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own
arXiv CS.LG
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ArXi:2310.02635v5 Announce Type: replace-cross Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For one thing, RL is data-intensive and typically requires millions of interactions with environments, which are impractical in real scenarios. For another, it is necessary to make heavy engineering efforts to design reward functions manually. To address these issues, we leverage foundation models in this paper.