Engineers help artificial intelligence to learn more safely in the real world

Penn State researchers are looking for a safer and more efficient way to use machine learning in the real world. Using a simulated high-rise office building, they developed and tested a new reinforcement learning algorithm aimed at improving energy consumption and occupant comfort in a real-world setting. Greg Pavlak, assistant professor of architectural engineering at Penn State, presented the results from the paper he co-authored, "Constrained Differentiable Cross-Entropy Method for Safe Model-Based Reinforcement Learning," at the Association for Computing Machinery International Conference on Systems for Energy-Efficient Built Environments (BuildSys) Conference, which was held Nov. 9-10 in Boston. "Reinforcement learning agents explore their environments to learn optimal actions through trial and error," Pavlak said. "Due to challenges in simulating the complexities of the real world, there is a growing trend to train reinforcement learning agents directly in the real world instead of in simulation."