Games can serve as important benchmarks for evaluating artificial intelligence (AI), and many game AIs have been invented, such as AlphaGo, Libratus, OpenAI Five, Suphx, and DouZero. For UNO, a popular shedding-type card game, the AI faces imperfect-information challenges, large state space, and sparse rewards. The existing methods only demonstrate the promising feasibility of classic reinforcement learning in the UNO game, which is still far from being able to compete with human players. Based on the Monte-Carlo method and deep neural networks, we combine it with Multi-Stage Learning during t...
