Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings - Subramanian, Sriram Ganapathi; Crowley, Mark (Canadian Conference on Artificial Intelligence, 2018)
In recent years, Deep Reinforcement Learning (RL) algorithms have shown super-human performance in a variety Atari and classic board games like chess and GO. Research into applications of RL in other domains with spatial considerations like environmental planning are still in their nascent stages. In this paper, we introduce a novel combination of Monte-Carlo Tree Search (MCTS) and A3C algorithms on an online simulator of a wildfire, on a pair of forest fires in Northern Alberta (Fort McMurray and Richardson fires) and on historical Saskatchewan fires previously compared by others to a physics-based simulator. We conduct several experiments to predict fire spread for several days before and after the given spatial information of fire spread and ignition points. Our results show that the advancements in Deep RL applications in the gaming world have advantages in spatially spreading real-world problems like forest fires.
Keywords: a3c; computational sustainability; fire; forest wild-; monte-carlo tree search; reinforcement learning