Reinforcement learning on the other hand explored the ideas of a self-sustained training process since its early models. DRL has wide applications such as robotics, self-driving cars, and hyperparameter tuning. RL together with generative attention models constitutes an important step towards the ultimate goal of the self-sustained learning process. This session attempts to address broad questions such as: “How to relate experience replay with the batch update?”, “How to make the state-action learning process deep?”, “How to make the architecture of an RL system appropriate to deep learning without compromising the interactivity of the system?”. Although recently there have been important advances in dealing with these issues, they are still scattered and with no overarching framework that promotes them in a well-defined and consistent way.

To submit your paper please choose our special session "Deep Reinforcement and Attention Models" in the research topic list. https://wcci2022.org/