4 papers
Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
Jannik Hösch, Alessandro Sestini, Florian Fuchs +6
Reinforcement learning (RL) has achieved strong performance in sequential decision-making, yet scaling to complex multi-agent environments remains challenging due to sparse rewards…
Self-correcting Reward Shaping via Language Models for Reinforcement Learning Agents in Games
António Afonso, Iolanda Leite, Alessandro Sestini +3
Reinforcement Learning (RL) in games has gained significant momentum in recent years, enabling the creation of different agent behaviors that can transform a player's gaming experi…
TROFI: Trajectory-Ranked Offline Inverse Reinforcement Learning
Alessandro Sestini, Joakim Bergdahl, Konrad Tollmar +2
In offline reinforcement learning, agents are trained using only a fixed set of stored transitions derived from a source policy. However, this requires that the dataset be labeled…
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning
Derek Yadgaroff, Alessandro Sestini, Konrad Tollmar +2
Imitation learning is an effective approach for training game-playing agents and, consequently, for efficient game production. However, generalization - the ability to perform well…