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...
Abstract: Reinforcement learning (RL) often faces the challenges of uninformed search problems where the agent should explore without access to the domain knowledge such as characteristics of the environment or external rewards. To tackle these challenges, this work proposes a new approach for cu...