Real World Problems through Deep Reinforcement Learning

Reinforcement Learning (RL) represents a very promising field in the umbrella of Machine Learning (ML). Using algorithms inspired by psychology, specifically by the Operant Conditioning of Behaviorism, RL makes it possible to solve problems from scratch, without any prior knowledge nor data about the task at hand. When used in conjuction with Neural Networks (NNs), RL has proven to be especially effective: we call this Deep Reinforcement Learning (DRL). In recent past, DRL proved super-human capabilities on many games, but its real world applications are varied and range from robotics to general optimization problems. One of the main focuses of current research and literature in the broader field of Machine Learning (ML) revolves around benchmarks, in a never ending challenge between researchers to the last decimal figure on certain metrics. However, having to pass some benchmark or to beat some other approach as the main objective is, more often than not, limiting from the point of view of actually contributing to the overall goal of ML: to automate as many real tasks as possible. Following this intuition, this thesis proposes to first analyze a collection of really varied real world tasks and then to develop a set of associated models. Finally, we apply DRL to solve these tasks by means of exploration and exploitation of these models. Specifically, we start from studying how using the score as target influences the performance of a well-known artificial player of Go, in order to develop an agent capable of teaching humans how to play to maximize their score. Then, we move onto machine creativity, using DRL in conjuction with state-of-the-art Natural Language Processing (NLP) techniques to generate and revise poems in a human-like fashion. We then dive deep into a queue optimization task, to dynamically schedule Ultra Reliable Low Latency Communication (URLLC) packets on top of a set of frequencies previously allocated for enhanced Mobile Broad Band (eMBB) users. Finally, we propose a novel DRL approach to the task of generating black-box Pseudo Random Number Generators (PRNGs) with variable periods, by exploiting the autonomous navigation of a state-of-the-art DRL algorithm both in a feedforward and a recurrent fashion.