Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

TL;DR

This paper introduces reincarnating RL, a framework for reusing prior computational work like learned policies to accelerate RL development, demonstrated through transfer algorithms and real-world applications.

Contribution

It proposes a novel approach for transferring sub-optimal policies to value-based RL agents, addressing limitations of existing methods and enabling more efficient RL workflows.

Findings

Reincarnating RL outperforms tabula rasa RL on Atari, locomotion, and balloon navigation tasks.

The proposed transfer algorithm effectively reuses prior policies in diverse environments.

Reincarnating RL can reduce training time and resource requirements.

Abstract

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. To address these issues, we present reincarnating RL as an alternative workflow or class of problem settings, where prior computational work (e.g., learned policies) is reused or transferred between design iterations of an RL agent, or from one RL agent to another. As a step towards enabling reincarnating RL from any agent to any other agent, we focus on the specific setting of efficiently transferring an existing sub-optimal policy to a standalone value-based RL agent. We find that existing approaches fail in this setting and propose a simple algorithm to address their limitations. 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 stratospheric balloons. Overall, this work argues for an alternative approach to RL research, which we believe could significantly improve real-world RL adoption and help democratize it further. Open-sourced code and trained agents at https://agarwl.github.io/reincarnating_rl.