Reinforcement learning, a subset of deep learning, relies on a model’s agent learning how to determine accurate solutions from its own actions and the results they produce in different states within a contained environment. This self-interpreting model is trained on a system of rewards and punishments learned through trial and error, seeking the outcome that results in the highest possible reward.
When to Use Reinforcement Learning
Reinforcement learning has many applications and is used in gaming, recommendation engines, robotics, traffic light control and more.
Reinforcement learning delivers proper next actions by relying on an algorithm that tries to produce an outcome with the maximum reward. This allows reinforcement learning to control the engines for complex systems for a given state without the need for human intervention.
Reinforcement learning is the most conventional algorithm used to solve games. A famous example of this is AlphaGo, a reinforcement learning engine that was trained in countless human games and has been able to defeat best-in-class masters of games renowned for their difficulty, such as Go, through the use of the Monte Carlo tree search and neural networks in its policy network.
Personalized recommendation engines use an advanced form of reinforcement learning known as deep reinforcement learning to overcome challenges like rapidly changing content, content fatigue and click rate to deliver recommendations with the greatest reward (i.e. a “yes” selection).
How Does Deep Reinforcement Learning Work?
Deep reinforcement learning combines reinforcement learning frameworks with artificial neural networks.
To help a software agent reach its reward, deep reinforcement learning combines reinforcement learning frameworks with artificial neural networks to map out a series of states and actions with the rewards they lead to, uniting function approximation and target optimization.
The inclusion of artificial neural networks allows reinforcement learning agents to tap into computer vision and time series prediction and facilitate real-time decision making that is based on a rewards and punishment system. Determining the best path to the maximum reward from a series of states and actions is responsible for AlphaGo and deep learning models besting top-tier human players in Atari video games such as Starcraft II and Dota-II, to name just a few examples.
What Kinds of Problems Can Reinforcement Learning Solve?
Reinforcement learning helps solve problems in expected and probabilistic environments.
In expected environments, an action must be executed in a certain order to produce a reward and will be punished if other orders are pursued.
Rewards in probabilistic environments, however, are harder to determine due to the inclusion of probability, and accordingly, determines the action that should be taken through a defined policy. A policy accounts for probability and determines the action that the agent should take based on the conditions of the environment.