3 accepted papers
When fine-tuning pre-trained Large Language Models (LLMs) to align with human values and intentions, maximizing the estimated reward can lead to superior performance, but it also introduces potential risks due to deviations from the reference model's intended behavior. Most existing methods typicall…
A challenging problem in seeking to bring multi-agent reinforcement learning (MARL) techniques into real-world applications, such as autonomous driving and drone swarms, is how to control multiple agents safely and cooperatively to accomplish tasks. Most existing safe MARL methods learn the centrali…
Reinforcement learning (RL) has made significant progress in areas such as Atari games and robotic control, where the agents have perfect sensing capabilities. However, in many real-world sequential decision-making tasks, the observation data could be noisy or incomplete due to the intrinsic low qua…