Exploration Policy

Exploration policy in reinforcement learning focuses on designing efficient strategies for agents to discover rewarding states or actions within complex environments, crucial for optimal decision-making. Current research emphasizes improving exploration efficiency in various contexts, including multi-agent systems, long-horizon tasks, and sparse-reward scenarios, often employing techniques like hierarchical reinforcement learning, Monte Carlo tree search, and meta-learning to optimize exploration-exploitation trade-offs. These advancements are significant for improving the sample efficiency and robustness of reinforcement learning algorithms across diverse applications, such as robotics, recommender systems, and autonomous navigation.