In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples. While recent works have been effective in leveraging past experiences for policy updates, they often overlook the potential of reusing past experiences for data collection. Independent of the underlying RL algorithm, we introduce the concept of a Contrastive Initial State Buffer, which strategically selects states from past experiences and uses them to initialize the agent in the environment in order to guide it toward more informative states. We validate our approach on two complex robotic tasks without relying on any prior information about the environment: (i) locomotion of a quadruped robot traversing challenging terrains and (ii) a quadcopter drone racing through a track. The experimental results show that our initial state buffer achieves higher task performance than the nominal baseline while also speeding up training convergence.

翻译：在强化学习中，探索与利用之间的权衡对从有限样本中实现高效学习构成了复杂挑战。尽管近期研究在利用过去经验更新策略方面卓有成效，但往往忽略了复用过往经验进行数据收集的潜力。我们独立于底层强化学习算法，提出了对比初始状态缓冲区的概念——该缓冲区从既往经验中策略性地选取状态，并利用这些状态初始化环境中的智能体，从而引导其朝向更富信息量的状态。我们在两个复杂机器人任务中验证了该方法，且无需依赖任何环境先验信息：（i）四足机器人在崎岖地形中的行走运动；（ii）四旋翼无人机沿赛道竞速飞行。实验结果表明，我们的初始状态缓冲区在加速训练收敛的同时，实现了比基准方法更高的任务性能。