Reinforcement Learning, Artificial Intelligence, Machine Learning, Data Science, Statistics.
Action and Consequence Dynamics: * Every action taken by an agent leads to both a reward and a change in the state. * The overarching goal for a reinforcement learning (RL) agent is to decipher the optimal sequence of actions that maximize cumulative rewards. * Prior to optimal behaviour, an agent