Optimal Behavior

Optimal behavior research investigates how agents, biological or artificial, learn and execute actions to maximize rewards or achieve goals within complex environments. Current research focuses on efficient exploration strategies (like satisficing approaches), robust offline learning from suboptimal data (using techniques like behavioral cloning and importance weighting), and the development of models that account for human suboptimality and partial observability (employing architectures such as transformers, diffusion models, and reward machines). These advancements have implications for improving reinforcement learning algorithms, creating more effective human-AI collaborations, and developing safer and more adaptable autonomous systems.