Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models
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.
A. Schwung, Steve Yuwono, Sofiene Lassoued et al.
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