Application of Reparameterization and Terminal Batch Processing Operators in NoGo

Deep reinforcement learning has achieved remarkable progress in the field of computer games, but these advancements come with significant computational resource consumption. To improve computational efficiency and reduce reliance on high computational resources, we applied two ingenious methods: reparameterization and terminal batch processing operators. By employing a specially designed replay buffer and neural networks integrated with reparameterization, and directly simulating end-game operations on the GPU, these methods not only reduce the computational burden but also ensure rapid convergence during the training process. This enhanced training framework allows for more iterations in a shorter period, thereby improving computational efficiency in self-play and training processes. We applied this technique to the game of NoGo. Experimental results indicate that, compared to the traditional AlphaZero method, the proposed approach achieves a faster increase in Elo score within the same timeframe. The agent utilizing this method secured first place among 1070 agents on the BotZone platform of Peking University.