HIRO represents "HIerarchical Reinforcement learning with Off-policy correction". The motivation of this paper is to train both HRL low-level policy and high-level policy with off-policy experience.
An overview of our research on agentic RL. In this work, we systematically investigate three dimensions of agentic RL: data, algorithms, and reasoning modes. Our findings reveal: Real end-to-end ...
Since its beginning back in 2015, Rocket League has become more and more popular in the esports scene, featuring the best Rocket League players. Naturally, as prize pools have grown, so have the ...
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