Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy industry. We have 6 premier panel discussions and 70+ great papers/posters. Our topics of interest are general, including (but not limited to): 1) practical RL algorithms, which covers all algorithmic challenges of RL, especially those that directly address challenges faced by real-world applications 2) practical issues: generalization, sample efficiency, exploration, reward, scalability, model-based learning, prior knowledge, safety, accountability, interpretability, reproducibility, hyper-parameter tuning and 3) applications.
We invite paper submissions successfully applying RL algorithms to real-life problems and/or addressing practically relevant RL issues. The main goals of the workshop are to: (1) identify key research problems that are critical for the success of real-world applications (2) report progress on addressing these critical issues and (3) have practitioners share their success stories of applying RL to real-world problems, and the insights gained from such applications. Why isn’t RL used even more often and how can we improve this? However, applying RL in the real world remains challenging, and a natural question is: RL has seen prominent successes in many problems, such as games, robotics, recommender systems. Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm and applies broadly in many disciplines, including science, engineering and humanities.
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