Training a Reinforcement Learning Agent for Tales of Tribute

Tales of Tribute is a strategic deck-building card game that presents unique challenges for artificial intelligence due to its complex action space, hidden information, and long-term planning requirements. In this work, we propose a reinforcement learning agent that learns to play Tales of Tribute without relying on handcrafted heuristics. Our approach introduces a generalizable game state representation and a scalable action evaluation mechanism based on preference vectors. We train our agent using Proximal Policy Optimization within the Scripts of Tribute framework and demonstrate competitive performance against established search-based agents, including Monte Carlo Tree Search. The results validate the viability of reinforcement learning in Tales of Tribute and highlight the potential of preference-based action evaluation in domains with large and variable action spaces. Further experiments will be required to test its viability in other card games.