Beyond Tabula Rasa: Reincarnating Reinforcement Learning

• URL: http://arxiv.org/abs/2206.01626v1
• Date: Fri, 3 Jun 2022 15:11:10 GMT
• Title: Beyond Tabula Rasa: Reincarnating Reinforcement Learning
• Authors: Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare
• Abstract summary: Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. We present reincarnating RL as an alternative workflow, where prior computational work is reused or transferred between design iterations of an RL agent. We find that existing approaches fail in this setting and propose a simple algorithm to address their limitations.
• Score: 37.201451908129386
• License: http://creativecommons.org/licenses/by/4.0/
• 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, 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.

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