Learning How Deep to Go: Self-Scaling Deep Reinforcement Learning
作者: Michelangelo Vegliò, Marco Fantozzi, Antonio Di Cecco, Carlo Metta, Flora Angileri, Simone Treccani, Adrienne Chloe Rayos Macazar, Silvia Giulia Galfrè, Maurizio Parton, Francesco Morandin
摘要
Deep Reinforcement Learning (DRL) has achieved remarkable results in complex sequential decision-making tasks, often using very deep neural networks. However, these architectures incur substantial computational and energy costs, and selecting the optimal network depth in advance remains an open challenge. In this paper, we introduce SCALE-RL, a self-scaling DRL framework that dynamically adjusts its architectural depth during training, allowing the network to automatically adapt its depth to the task. Integrated into an AlphaZero-style pipeline for Othello, our approach matches the playing strength of the baseline agent while reducing network depth by 50