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Awesome GitHub RepositoriesOptimizer Configurations
Interfaces for selecting and tuning optimization algorithms and parameters for policy training.
Distinct from Optimizer Configurations: Distinct from Reinforcement Learning Optimizers: focuses on the configuration and selection of optimizers rather than the implementation of the optimization algorithms themselves.
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Awesome Optimizer Configurations GitHub Repositories
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- dlr-rm/stable-baselines3
DLR-RM/stable-baselines3
12,765Auf GitHub ansehenStable-baselines3 is a reinforcement learning library built on the PyTorch deep learning framework. It provides a collection of reliable, standardized implementations of reinforcement learning algorithms designed for training, testing, and benchmarking agent policies in diverse simulated environments. The library functions as an agent training toolkit that emphasizes modularity and reproducibility. It features a unified environment interface and supports vectorized execution to accelerate data collection across multiple simulation instances. Users can customize neural network architectures, f
Allows customization of the optimization process by selecting specific optimizer classes and parameters.
Pythonbaselinesgsdegym
