Free Exploration

Free exploration in reinforcement learning focuses on developing efficient algorithms that allow agents to effectively discover and map unknown environments, a crucial challenge for autonomous systems. Current research emphasizes model-free approaches, employing techniques like barycentric spanners for efficient exploration in high-dimensional spaces and deep reinforcement learning architectures for task and motion planning directly in feature spaces, often leveraging imitation learning from expert demonstrations. These advancements improve sample efficiency and generalization capabilities, leading to more robust and adaptable autonomous agents for applications such as robotics and navigation in complex, unstructured environments.