Training an agent to operate in an environment whose mappings are largely unknown is generally recognized to be exceptionally difficult. Further, granting such a learning agent the ability to produce an appropriate sequence of actions entirely from a single input stimulus remains a key problem. Various reinforcement learning techniques have been utilized to handle such learning tasks, but convergence to optimal policies is not guaranteed for many of these methods. Traditional supervised learning methods hold more assurances of convergence, but these methods are not well suited for tasks where desired actions in the output space of the learner, termed proximal actions, are not available for training. Rather, target outputs from the environment are distal from where the learning takes place. For example, a child acquiring language who makes speech errors must learn to correct them based on heard information that reaches his/her auditory cortex which is distant from the motor cortical regions that control speech output. While distal supervised learning techniques for neural networks have been devised, it remains to be established how they can be trained to produce sequences of proximal actions from only a single static input. In this research, I develop an architecture which incorporates recurrent multi-layered neural networks that possess some form of history in the form of a context vector into the distal supervised learning framework, enabling it to learn to generate correct proximal sequences from single static input stimuli. This is in contrast to existing distal learning methods designed for non-recurrent neural network learners that utilize no concept of memory of their prior behavior. Also, I adapt a technique in this research known as teacher forcing for use in distal sequential learning settings which is shown to result in more efficient usage of the recurrent neural network's context layer. The effectiveness of my approach is demonstrated by applying it to acquire varying length phoneme sequence generation behavior using only previously heard and stored auditory phoneme sequences. The results indicate that simple recurrent backpropagation networks can be integrated with distal learning methods to create effective sequence generators even when they do not constantly update current state information.