论文标题
内在机器人内省：从神经元激活中学习内部状态
Intrinsic Robotic Introspection: Learning Internal States From Neuron Activations
论文作者
论文摘要
我们提出了一个内省的框架，灵感来自人类的内省过程。我们的工作假设是，神经网络激活编码信息，并且从这些激活中构建内部状态可以改善参与者批评模型的性能。我们执行实验，首先训练变异自动编码器模型以重建特征提取网络的激活，并在决定执行哪种低级机器人行为时使用潜在空间来提高参与者批评的性能。我们表明，内部状态在训练演员批评的同时将所需的发作数量减少了大约1300集，表明更快的融合以在完成机器人任务的同时获得很高的成功价值。
We present an introspective framework inspired by the process of how humans perform introspection. Our working assumption is that neural network activations encode information, and building internal states from these activations can improve the performance of an actor-critic model. We perform experiments where we first train a Variational Autoencoder model to reconstruct the activations of a feature extraction network and use the latent space to improve the performance of an actor-critic when deciding which low-level robotic behaviour to execute. We show that internal states reduce the number of episodes needed by about 1300 episodes while training an actor-critic, denoting faster convergence to get a high success value while completing a robotic task.
