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About this paper
Nis Meinert, Jakob Gawlikowski, Edward Grant, Max Cyprys, Nathan Schurr, Neil Boston, Alfredo Ibias, Milind Tambe and 1 more
The paper maps continual reinforcement learning around ten desiderata for agents that keep learning during deployment without task boundaries or resets. The authors review complementary research and report no known published evaluation integrating all ten under the target protocol and resource budget. They also propose a playable persistent-world benchmark, but its calibration and agent comparisons remain future work.
Summary by alphaXiv.
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