Conservative Exploration

Conservative exploration in reinforcement learning focuses on developing algorithms that guarantee a learning agent's performance remains above a predefined threshold, preventing catastrophic failures during exploration. Current research emphasizes model-free and model-based approaches, employing techniques like importance sampling, mixture policies, and adaptive model generation to achieve this constraint while maintaining near-optimal learning efficiency. This research area is crucial for safely deploying reinforcement learning agents in real-world applications where performance guarantees are paramount, impacting fields such as robotics and autonomous systems.