“Neural MCTS algorithms are a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS) and have successfully trained Reinforcement Learning agents in a tabula-rasa way. … However, these algorithms … take a long time to converge, which requires high computational power and electrical energy. It also becomes difficult for researchers without cutting-edge hardware to pursue Neural MCTS research. We propose Step-MCTS, a novel algorithm that uses subnet structures, each of which simulates a tree that provides a lookahead for exploration.”

Find the paper and full list of authors in the International Conference on Autonomous Agents and Multiagent Systems proceedings.