Explorative learning of right inverse functions: theoretical implications of redundancy

We investigate the role of redundancy for exploratory learning of inverse functions, where an agent learns to achieve goals by performing actions and observing outcomes. We present an analysis of linear redundancy and investigate goal-directed exploration approaches, which are empirically successful [1], but hardly theorized except negative results for special cases [2], and prove convergence to the optimal solution.