Author: MarkTechPost

Despite recent advances in RL research, the ability to generalize to new tasks remains one of the major issues in both reinforcement learning (RL) and decision-making. RL agents perform remarkably in a single-task setting but frequently make mistakes when faced with unforeseen obstacles. Additionally, single-task RL agents can largely overfit the tasks they are trained on, rendering them unsuitable for real-world applications. This is where a general agent that can successfully handle various unprecedented tasks and unforeseen difficulties can be useful. The vast majority of general agents are trained using a variety of diverse tasks. Recent deep-learning research has shown