Reinforcement Learning Objective

Reinforcement learning (RL) objective design focuses on defining how agents learn optimal behaviors, balancing reward maximization with constraints like safety and robustness. Current research emphasizes developing objectives that handle multiple, potentially conflicting goals (multi-objective RL), incorporate risk awareness, and improve sample efficiency through techniques like transition occupancy matching and fine-grained reward modeling. These advancements are crucial for deploying RL in complex, real-world scenarios, particularly in safety-critical applications and for improving the efficiency and reliability of training large language models.