至于本文提到的重生强化，所提出的算法PVRL（policy (+data) to value RL），他们和上述五个方案的异同点在于，和offline RL一样，利用teacher policy的data做了离线预训练，后面的在线 …

2022年6月3日 · Equipped with this algorithm, we demonstrate reincarnating RL's gains over tabula rasa RL on Atari 2600 games, a challenging locomotion task, and the real-world …

2022年11月3日 · To address the inefficiencies of tabula rasa RL, we present “Reincarnating Reinforcement Learning: Reusing Prior Computation To Accelerate Progress” at NeurIPS …

2022年10月31日 · Equipped with this algorithm, we demonstrate reincarnating RL's gains over tabula rasa RL on Atari 2600 games, a challenging locomotion task, and the real-world …

2022年12月14日 · 至于本文提到的重生强化，所提出的算法PVRL（policy (+data) to value RL），他们和上述五个方案的异同点在于，和offline RL一样，利用teacher policy的data做了 …

Equipped with this algorithm, we demonstrate reincarnating RL’s gains over tabula rasa RL on Atari 2600 games, a challenging locomotion task, and the real-world problem of navigating …

Equipped with this algorithm, we demonstrate reincarnating RL's gains over tabula rasa RL on Atari 2600 games, a challenging locomotion task, and the real-world problem of navigating …

To address the inefficiencies of tabula rasa RL and help unlock the full potential of deep RL, this workshop would focus on the alternative paradigm of leveraging prior computational work, …

This work argues for an alternative approach to RL research, where we build on prior computational work, which we believe could significantly improve real-world RL adoption and …

Learning “tabula rasa”, that is, from scratch without much previously learned knowledge, is the dominant paradigm in reinforcement learning (RL) research. However, learning tabula rasa is …