Experiments for "Entropy Based Regularization Improves Performance in Forward-Forward Algorithm"
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Updated
Nov 3, 2025 - Jupyter Notebook
Experiments for "Entropy Based Regularization Improves Performance in Forward-Forward Algorithm"
End-to-end Python implementation of Huang's (2025) continuous-time RL methodology for asset-liability management. Features model-free soft actor-critic with adaptive exploration, entropy regularization, and Euler-Maruyama SDE simulation. Includes 7 baselines (SAC/PPO/DDPG/CPPI/ACS/MBP), parallelized execution, and Wilcoxon statistical validation.
🤖 Leverage continuous-time reinforcement learning to optimize asset-liability management and enhance financial decision-making.
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