论文标题
机器人探索高维感觉空间的内在动机和情节记忆
Intrinsic Motivation and Episodic Memories for Robot Exploration of High-Dimensional Sensory Spaces
论文作者
论文摘要
这项工作提出了一种架构，该体系结构为微型机器人的图像传感器生成了好奇心驱动的目标探索行为。深度神经网络的结合使用了来自图像的低维特征的无监督学习，以及对代表系统的逆向运动学的浅神经网络的在线学习。人工好奇心系统将兴趣价值分配给一组预定义的目标，并将探索驱动到那些有望最大化学习进度的人。我们建议将情节记忆的整合在内在动机系统中面对灾难性遗忘问题，这通常在执行人工神经网络的在线更新时经历。我们的结果表明，采用情节内存系统不仅可以防止计算模型快速忘记以前获取的知识，而且还提供了调节模型可塑性和稳定性之间平衡的新途径。
This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of low-dimensional features from images, and of online learning of shallow neural networks representing the inverse and forward kinematics of the system have been used. The artificial curiosity system assigns interest values to a set of pre-defined goals, and drives the exploration towards those that are expected to maximise the learning progress. We propose the integration of an episodic memory in intrinsic motivation systems to face catastrophic forgetting issues, typically experienced when performing online updates of artificial neural networks. Our results show that adopting an episodic memory system not only prevents the computational models from quickly forgetting knowledge that has been previously acquired, but also provides new avenues for modulating the balance between plasticity and stability of the models.
