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Replaying data is a principal mechanism underlying the stability and dataefficiency of off-policy reinforcement learning (RL). We present an effectiveyet simple framework to extend the use of replays across multiple experiments,minimally adapting the RL workflow for sizeable improvements in controllerperformance and research iteration times. At its core, Replay AcrossExperiments (RaE) involves reusing experience from previous experiments toimprove exploration and bootstrap learning while reducing required changes to aminimum in comparison to prior work. We empirically show benefits across anumber of RL algorithms and challenging control domains spanning bothlocomotion and manipulation, including hard exploration tasks from egocentricvision. Through comprehensive ablations, we demonstrate robustness to thequality and amount of data available and various hyperparameter choices.Finally, we discuss how our approach can be applied more broadly acrossresearch life cycles and can increase resilience by reloading data acrossrandom seeds or hyperparameter variations.

Replay across Experiments: A Natural Extension of Off-Policy RL