Sparse Reward Continuous Control

Sparse reward continuous control in reinforcement learning focuses on training agents to perform complex tasks where positive feedback is infrequent, a common challenge in real-world robotics and other applications. Current research emphasizes improving exploration strategies, often incorporating intrinsic motivation or advanced experience replay techniques within deep deterministic policy gradient (DDPG) and hierarchical reinforcement learning (HRL) frameworks. These advancements aim to enhance sample efficiency and overcome the difficulties posed by sparse rewards, leading to more robust and data-efficient learning algorithms for continuous control problems. The resulting improvements have significant implications for deploying reinforcement learning in real-world scenarios where obtaining frequent rewards is impractical or impossible.