Andreas Schwung

A novel framework for model-based reinforcement learning which introduces approximate inverse process models within the training of reinforcement policies, and proposes a lightweight feedforward architecture for approximate inverse models and integrate them within the policy network of standard RL algorithms.

Injecting data-derived spectral priors into neural network initialization can dramatically accelerate convergence and improve the efficiency of function parameterizing architectures.