Network Layer Analysis for a RL-Based Robotic Reaching Task

Recent experiments indicate that pretraining of end-to-end reinforcement learning neural networks on general tasks can speed up the training process for specific robotic applications. However, it remains open if these networks form general feature extractors and a hierarchical organization that can be reused as in, for example, convolutional neural networks. In this study, we analyze the intrinsic neuron activation in networks trained for target reaching of robot manipulators with increasing joint number and analyze the individual neuron activation distribution within the network. We introduce a pruning algorithm to increase network information density and depict correlations of neuron activation patterns. Finally, we search for projections of neuron activation among networks trained for robot kinematics of different complexity. As a result, we show that the input and output network layers entail more distinct neuron activation in contrast to inner layers. Our pruning algorithm reduces the network size significantly and increases the distance of neuron activation while keeping a high performance in training and evaluation. Our results demonstrate that robots with small difference in joint number show higher layer-wise projection accuracy, whereas more distinct robot kinematics reveal dominant projections to the first layer.