Advancements in Reinforcement Learning Algorithms

Meta-learning plays a crucial role in developing more effective reinforcement learning (RL) algorithms. Researchers have focused on optimizing the update rule—a key component of RL algorithms—by applying meta-learning techniques to various environments. This approach has shown promise in improving performance through learning which update rules are most effective. Despite the advancement in meta-learning, human design still plays an important role, as researchers can enhance proxy objectives that significantly impact algorithm effectiveness.