Projects

Enhancing Multi-task Generalization in Multi-agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), achieving multi-task generalization to diverse agents and objectives presents significant challenges. Existing online MARL algorithms primarily focus on single-task performance, but their lack of multi-task generalization capabilities typically results in substantial computational waste and limited real-life applicability. Meanwhile, existing offline multi-task MARL approaches are heavily dependent on data quality, often resulting in poor performance on unseen tasks. Therefore, we want to develop effective Multi-task MARL methods that can achieve generalization across different tasks with little training costs. One related paper is HyGen.
Enhancing Multi-task Generalization in Multi-agent Reinforcement Learning
Efficient Reinforcement Learning for Solving Multiple Tasks
Reinforcement Learning (RL) has gained significant attention for its potential to solve complex problems in areas such as robotics, sensor networks, and gaming. However, most recent RL approaches focus primarily on learning policies for a single task in simulated environments. This focus can create substantial challenges when transitioning to real-world applications, as the high costs and risks associated with interactions under partially learned policies can be prohibitive. Consequently, enabling agents to solve multiple tasks within a single training process is crucial for the practical deployment of RL in real-world scenarios.
Efficient Reinforcement Learning for Solving Multiple Tasks