Nicholas Edward Corrado - Distributionally Robust Multi-Task RL via Adaptive Task Sampling

Talk Description: (Paper link: https://arxiv.org/abs/2605.14350) Multi-task reinforcement learning (MTRL) aims to train a single agent to efficiently optimize performance across multiple tasks simultaneously. However, jointly optimizing all tasks often yields imbalanced learning: agents quickly solve easy tasks but learn slowly on harder ones. While prior work primarily attributes this imbalance to conflicting task gradients and proposes gradient manipulation or specialized architectures to address it, we instead focus on a distinct and under-explored challenge: imbalanced data allocation. Sta