Improving Learning to Branch via Reinforcement Learning
Branch-and-Bound~(B\&B) is a general and widely used algorithm paradigm for solving Mixed Integer Programming~(MIP). Recently there is a surge of interest in designing learning-based branching policies as a fast approximation of strong branching, a human-designed heuristic. In this work, we argue strong branching is not a good expert to imitate for its poor decision quality when turning off its side effects in solving linear programming. To obtain more effective and non-myopic policies than a local heuristic, we formulate the branching process in MIP as reinforcement learning~(RL) and design a policy characterization for the B\&B process to improve our agent by novelty search evolutionary strategy. Across a range of NP-hard problems, our trained RL agent significantly outperforms expert-designed branching rules and the state-of-the-art learning-based branching methods in terms of both speed and effectiveness. Our results suggest that with carefully designed policy networks and learning algorithms, reinforcement learning has the potential to advance algorithms for solving MIPs.
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