Abstract: Multi-task reinforcement learning (MTRL) offers a promising approach to improve sample efficiency and generalization by training agents across multiple tasks, enabling knowledge sharing between them. However, applying MTRL to robotics remains challenging due to the high cost of collecti...
Abstract: A fully autonomous agent should reason about how to deploy limited resources effectively in dynamic and uncertain environments. Despite the focus on learning to act under such constraints, the tactical use of resources in fast-evolving scenarios (e.g., air combat) remains underexplored....
Abstract: Despite the remarkable success of reinforcement learning (RL) in mastering intricate skills through environmental interactions, the conventional assumption of easily accessible resets at the end of each episode poses challenges for autonomous learning in real-world scenarios. This assum...