Novelty-based Sample Reuse for Continuous Robotics Control
Novelty-based Sample Reuse for Continuous Robotics Control
In reinforcement learning, agents collect state information and rewards through environmental interactions, essential for policy refinement. This process is notably time-consuming, especially in complex robotic simulations and real-world applications. Traditional algorithms usually re-engage with the environment after processing a single batch of samples, thereby failing to fully capitalize on historical …