Community Blog Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

Reinforcement learning is one major type in machine learning, in this article, we will make a brief introduction on reinforcement learning.

Machine learning is one of the subtopics of Artificial Intelligence which is one of the hottest topics nowadays. And machine learning can be divided into three major types, which are supervised learning, unsupervised learning, and reinforcement learning.

Reinforcement learning models are very similar to how someone trains a dog. That is, the way you train a dog is you give it a treat whenever it performs a target task. In these algorithms, the dog is represented by an agent, and the doggie treat represents the reward. Next, there is the action or the target task you want the dog to perform. Next, two important elements are the enrvironment and the interpreter. They can be understood at the are where the dog performs the target tasks and the person who commands and rewards the dog with treats, respectively.

Technically speaking, reinforcement learning algorithms can be explained as the type of machine learning model where tasks are performed by an agent in a particular environment. In this model, the agent either receives a reward or punishment for each task performed. As the name suggests, it is a process of continuous improvements based on some rules. Unlike other machine learning approaches the algorithm is not told how to perform a task but learns by itself.

Reinforcement learning models require a lot of data and therefore work where data is readily available such as in gameplay and robotics scenarios. Reinforcement learning has been applied to board games, such as backgammon, checkers, and chess. The results of reinforcement learning models can be tested by testing the teaching efficient of the agent against a human being.

Some important reinforcement learning algorithms are:

  1. Monte Carlo
  2. Q-Learning
  3. State Action Reward State Action (SARSA)
  4. Deep Q Network (DQN)
  5. Asynchronous Actor-Critic Agent (A3C)
  6. Deep Deterministic Policy Gradient (DDPG)
  7. NAF

For information about Supervised Learning and Semi-Supervised Learning, please go to A Closer Look into the Major Types of Machine Learning Models.

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In terms of methodology, the Libratus algorithm is a combination of both Game Theory and operations research. Markov decision making process and dynamic planning comprise the theoretical foundation of reinforcement learning. Even though the sources are different, the two will eventually converge.

I believe that the collision of the Libratus algorithm with reinforcement learning and deep learning will be an amazing step forward for AI.

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