Learning to walk - Reward relevance within an enhanced neuroevolution approach

Recent advances in human motion sensing technologies and machine learning have enhanced the potential of AI to simulate artificial agents exhibiting human-like movements. Human movements are typically explored via experimental recordings with the aim of establishing relationships between neural and mechanical activities. A recent trend in AI research shows that AI algorithms work remarkably well when combined with sufficient computing resources and data. One common criticism is that all of these methods are gradient-based, which involves a gradient approximation, thus suffering of the well-known vanishing gradient problem. In this paper, the goal is to build an ANN-based controller that enables an agent to walk in a human-like way. In particular, the proposed methodology is based on a new approach to Neuroevolution based on NeuroEvolution of Augmenting Topologies (NEAT). The original algorithm has been endowed with a different type of selection-reproduction mechanism and a modified management of the population, with the aim to improve the performance and to reduce the computational effort. Experiments have evidenced the effectiveness of the proposed approach and have highlighted the interdependence among three key aspects: the reward framework, the Evolutionary Algorithm chosen and the hyper-parameters' configuration. As a consequence, none of the above aspects can be ignored and their balancing is crucial for achieving suitable results and good performance