Exploration and value function factorisation in single and multi-agent reinforcement learning - Rashid, T (2021)
The ability to learn from data is crucial in developing satisfactory solutions to many complex problems. In particular, in the design of intelligent agents that exist and interact with a complex environment in the pursuit of some goal. In this thesis we investigate some bottlenecks that can prevent such agents from learning and making progress in their respective environments. To do so we adopt the framework of Reinforcement Learning (RL), specifically Deep RL in which deep neural networks are used to learn and represent functions and quantities of interest.