DouMH: Improving DouZero with Multiple Heads

Deep reinforcement learning has made remarkable strides in gaming, demonstrating prowess across perfect and imperfect information scenarios. However, its efficacy in the card game DouDiZhu has been less than optimal. DouDiZhu stands out from other games due to its multiplayer nature, blending competitive and cooperative dynamics, which translates to a complex state-action space. In 2021, DouZero emerged, leveraging Monte Carlo methods and neural networks to surpass numerous counterparts. Yet, while Monte Carlo methods boast swift training, they introduce significant variance, leading to instability during training and gameplay. To tackle this challenge, our study integrates multiple supervision heads into the neural network and enhances DouZero's architecture. Our findings reveal that our agent, DouMH, notably enhances the win rate within the same timeframe, marking agent to achieve over a 60% win rate against DouZero.