Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. (arXiv:2206.01626v2 [cs.LG] UPDATED)

Learning tabula rasa, that is without any prior knowledge, is the prevalent
workflow in reinforcement learning (RL) research. However, RL systems, when
applied to large-scale settings, rarely operate tabula rasa. Such large-scale
systems undergo multiple design or algorithmic changes during their development
cycle and use ad hoc approaches for incorporating these changes without
re-training from scratch, which would have been prohibitively expensive.
Additionally, the inefficiency of deep RL typically excludes researchers
without access to industrial-scale resources from tackling
computationally-demanding problems. …

arxiv computation prior progress reinforcement reinforcement learning