Reinforcement learning for optimal control problems in continuous time and space
This thesis considers the problem of finding an optimal control policy for control problems in continuous time and space, utilising reinforcement learning techniques. Finding analytical solutions to optimal control problems is often intractable due to involved constraints, cost-function objectives, and uncertainty. The focal point will be on reinforcement learning RL techniques to find approximate
