Shypd.aiThe largest open index of AI toolsDeep Symbolic Optimization (DSO) is a deep learning framework for symbolic optimization tasks. It supports symbolic regression and discovering symbolic policies for reinforcement learning environments.
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Deep Symbolic Optimization (DSO) is a robust deep learning framework designed for various symbolic optimization tasks. The core package, `dso`, provides algorithms for symbolic regression, which involves recovering tractable mathematical expressions from input datasets, and for discovering symbolic policies within reinforcement learning environments. The framework offers a simple interface for defining new tasks and has been benchmarked against SRBench, achieving state-of-the-art results in symbolic solution rate and accuracy. It also won 1st place in the Real-World Track of the 2022 SRBench Symbolic Regression Competition. DSO supports configuration via JSON files, allowing users to customize experiments, tasks, logging, and hyperparameters for both regression and control tasks. A modern PyTorch-based refactor is also available.
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