Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

Understand

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.