This repo is for the paper: Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration
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For computing the optimal DAC policy for RLS (Randomized Local Search) on LeadingOne problem, please check the instructions in rls_lo/optimal_policy/.
For training/evaluating a DDQN agent on this benchmark, please see the instructions in rls_lo/rl.
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banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services. This library is developed by Bandit ML and ex-authors of Facebook's app
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