Exploring Generalization in Deep Reinforcement Learning algorithms for different tasks using Gymnasium, Gymnasium-Robotics and MuJoCo

Codebase for paper: Toward Zero-Shot Generalization of Deep Reinforcement Learning for Hybrid Energy System Dispatch

The code & files for the article "Metrics for Assessing Generalization of Deep Reinforcement Learning in Parameterized Environments".

Investigating DRL DCVAE} for mitigating 15% domain shift in cardiac MRI segmentation. Conclusively proves the Information Bottleneck trade-off: 0.02 generalization gap vs. 0.25 DSC performance. Structural limits of DRL identified for high-detail medical tasks.

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This study aims to develop a deep reinforcement learning (DRL) framework that separately models and jointly optimizes visible and spectral information, thereby enhancing the transparency, interpretability, and fairness of multimodal representation learning while improving its generalization capability.