AI Research
CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning
A critic-guided approach to reusable data selection that slashes compute costs without sacrificing model accuracy.
Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, while naive subsampling can remove rare transitions needed for long-horizon credit assignment. We introduce CODS, a critic-guided...
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ArXiv CS.LG
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ArXiv CS.LG
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CODS optimizes offline reinforcement learning by creating efficient, static data subsets that maintain performance while drastically reducing computational overhead. This approach solves the redundancy problem in training, enabling more scalable experiments across various complex environments.
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