Reinforcement learning (RL) is an area of machine learning, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, reinforcement learning is called approximate dynamic programming, or neuro-dynamic programming. The problems of interest in reinforcement learning have also been studied in the theory of optimal control, which is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation, particularly in the absence of a mathematical model of the environment. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov Decision Process (MDP).
Using Machine Learning Agents Toolkit in a real game: a beginner’s guide – The initial algorithm we came up with was allowing the agent to earn reward if, when not in a situation of danger, its distance from the target was decreasing. Similarly, when in a situation of danger, it would earn reward if its distance from the target was increasing (the agent was “running away”). Additionally, the algorithm was including a punishment given to the agent in case it was attacking when not allowed to.
OpenAI Gym – a toolkit for developing and comparing reinforcement learning algorithms.