论文标题
稀疏奖励的机器人操纵任务的基于授权的解决方案
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards
论文作者
论文摘要
为了为机器人操作提供自适应和用户友好的解决方案，即使仅提供非常稀疏的说明信号，代理商也必须学会完成任务。为了解决在任务奖励稀疏时面临的强化学习算法的问题，本文提出了一种内在动机方法，可以轻松地将其集成到任何标准的强化学习算法中，并可以允许机器人操纵器学习有用的操纵技巧，而只有稀疏的额外奖励。通过整合和平衡授权和好奇心，与在广泛的经验测试期间其他最新的内在探索方法相比，这种方法表现出较高的性能。定性分析还表明，当与多样性驱动的内在动机结合使用时，这种方法可以帮助操纵者学习一系列不同的技能，这些技能有可能应用于其他更复杂的操纵任务并加速其学习过程。
In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. Qualitative analysis also shows that when combined with diversity-driven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.
