Reinforced Learning for strategic Card Game

Abstract

Reinforcement learning (RL) techniques have shown promise in developing intelligent agents for complex games. This paper presents an application of RL to a strategic card game environment using the Godot game engine. We detail the design of an RL-based agent that learns optimal moves in the card game through trial-and-error interactions, along with the integration of the Godot engine to simulate the game environment. The agent's learning process leverages Q-learning and deep reinforcement learning principles to handle the large state space of cards and game situations. We describe the training process, including state representation, reward design, and the use of self-play to improve policy learning. Experimental results demonstrate that the agent improves its performance over time, achieving a competitive win rate against baseline strategies. This study highlights how game engines like Godot can facilitate the development and training of RL agents in interactive games. The approach and findings can guide future research on applying reinforcement learning methods to complex, discrete decision-making environments such as card games