Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search скачать в хорошем качестве

Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search

Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search This document introduces AlphaChute, a novel algorithm designed to achieve superhuman performance in the ancient game of Chutes and Ladders, also known as Moksha Patam. The algorithm leverages deep neural networks and tree search methodologies to navigate the game effectively. A significant breakthrough presented is the formal proof that AlphaChute converges to the Nash equilibrium in constant time, positioning it as the first formal solution to this game. Despite its advanced capabilities, the authors note that the implementation of AlphaChute remains relatively straightforward due to domain-specific adaptations. The paper also highlights the surprising lack of deep learning applications to Chutes and Ladders, a game historically used for teaching morality. This moral teaching aspect is implicitly linked to discussions around the development of artificial general intelligence (AGI). The full source code for AlphaChute is made available in the document's appendix. #AlphaChute #ChutesAndLadders #DeepLearning #NeuralNetworks #TreeSearch #NashEquilibrium #AI #GamingAI #SuperhumanPerformance #MokshaPatam paper - https://arxiv.org/pdf/2104.00698v1 subscribe - https://t.me/arxivpaper donations: USDT: 0xAA7B976c6A9A7ccC97A3B55B7fb353b6Cc8D1ef7 BTC: bc1q8972egrt38f5ye5klv3yye0996k2jjsz2zthpr ETH: 0xAA7B976c6A9A7ccC97A3B55B7fb353b6Cc8D1ef7 SOL: DXnz1nd6oVm7evDJk25Z2wFSstEH8mcA1dzWDCVjUj9e created with NotebookLM