Reinforcement learning (RL) is an area of machine learning associated with agents willing to take actions in an environment in order to maximize the notion of a collective end reward. Games and the concept behind them have always had fragments of reinforcement learning and in ways, gaming domains are testing grounds for complex reinforcement algorithms.
So what is the Reinforcement Theory?
Historically it is a concept based on Markov decision processes in which a combination of action in a particular state of the environment entirely determines the possibility of getting a particular amount of reward as well as determining the change of state. Simply put reinforcement learning is all about the ability to associate different types of stimuli and the occurrence of rewards or punishments as a result. The computational and psychological views share the basic idea that the learner wants something also termed as reward-seeking behavior.
A typical RL algorithm operates with only limited information on the environment and limited feedback on the quality of the decisions. To operate effectively, learners require the ability to formulate the process and refine their ideas towards attaining the reward.
In several games, the best players effectively use reinforcement learning to achieve their end goals and proceed to the next level. There has been a significant application of machine learning in games such as car racing, Doom, Minecraft, and StarCraft. As in the figure below, the agent is the player who continues to route the environment using various approaches, refining it at every cycle until the reward is achieved.
Deep Reinforcement Learning
Deep Machine Learning when combined with Reinforcement Learning forms the DRL. Deep learning is a part of machine learning in artificial intelligence that has artificially developed neural network algorithms similar to that of a human brain to learn from large amounts of data. This trains and allows machines to solve problems where data available can be very unstructured and diverse. Few practical examples of Deep Learning include Virtual Assistants, face recognition, etc.
Not to cause an information overload, let’s continue this series in multiple stories.