Reinforcement Learning
- Reinforcement learning can be understood using the concepts of agents, environments, states, actions and rewards.
Reinforcement learning (RL) is an area of machine learning concerned with how agents ought to take actions in an environment, as to maximize some notion of cumulative reward. Reinforcement Learning (RL) itself is a machine learning method inspired by behaviorist psychology and in particular,the way humans and animals learn to take decisions via (positive or negative) rewards received by their environment.
In reinforcement learning, the training signal of the algorithm is provided by the environment based on how an agent is interacting with it. At a particular point in time (T),the agent is in a particular state (S) and take an action (A) from all the available actions in its current state. As a response, the environment delivers an immediate reward, (R).
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. 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.