Entropy-regularized Soft Actor-Critic (SAC) exploration engine balancing expected return and policy entropy to prevent early convergence.
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Updated
Sep 10, 2026 - Python
Entropy-regularized Soft Actor-Critic (SAC) exploration engine balancing expected return and policy entropy to prevent early convergence.
Entropy-regularized Soft Actor-Critic (SAC) exploration engine balancing expected return and policy entropy to prevent early convergence.
Experiments for "Entropy Based Regularization Improves Performance in Forward-Forward Algorithm"
Tensorflow framework implementing classical and advanced state-space filtering methods, including Kalman filters, particle filters, particle flow (EDH/LEDH, invertible PF-PF, kernel-embedded), and differentiable particle filtering with entropy-regularized optimal transport (Sinkhorn).
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.
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