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Reinforcement Learning: A Simple Yet Powerful Technique


Reinforcement learning is a machine learning technique used to learn how to navigate through labyrinths, play video games, or teach digital creatures how to walk. The goal is to determine a sequence of actions that are considered optimal in a given environment. In this article, we’ll explore the concepts behind reinforcement learning and discuss the recent development in the field.

The Intuition Behind Reinforcement Learning

Despite the complexity of the mathematical formulations used in reinforcement learning, the intuition behind the algorithm itself is straightforward. The process is to choose an action and observe whether it produces a reward. If it does, then the action should be repeated; if the reward is not received, then a different action should be tried. The reward could be in the form of a score in a computer game or how far a digital creature can walk.

Challenges with Learning through Reward-Based Systems

Learning through reward-based systems is not an easy task. The reward often comes long after the action, making it challenging for the machine to understand when a particular action was helpful. Hence, it’s not surprising that reinforcement learning techniques do not excel in strategy games that require long-term planning. In these games, good plays usually require strategies that are developed over time.

Recent Development in Reinforcement Learning

Google DeepMind is one of the leading proponents of reinforcement learning. They have been working on techniques to improve the algorithm’s ability in long-term planning, including curiosity-based approaches that help in better planning remarkably. However, as the number of techniques in this domain increases, it is becoming clear that we need a framework to test and evaluate these techniques and create standardized benchmarks.

OpenAI Gym: A Unified Framework for Reinforcement Learning

OpenAI is a non-profit organization committed to developing ethical and transparent machine learning techniques. The company recently launched Gym, a unified framework for reinforcement learning techniques. The framework allows anyone to submit their solutions, which can be run on the same problems. Leaderboards are created to rank the solutions, and reference solutions are provided as a starting point. The environments in Gym range from computer games to different balancing tasks.

The Advantages of Gym

Gym is like Disneyworld for someone who is passionate about reinforcement learning. It offers a unified platform for testing different approaches against each other. This establishes a healthy competition within the field, which encourages researchers to develop better techniques. As more people participate, we can expect better and more sophisticated algorithms to emerge. This is an exciting prospect for consumers, as it means that the quality of machine learning applications will continue to improve.


In conclusion, reinforcement learning is a powerful technique that is becoming increasingly popular in the machine learning domain. The algorithm’s intuition is straightforward, but the challenge is in optimizing the process to achieve optimal results. Recent developments, such as OpenAI Gym, are helping to improve the algorithm’s ability to learn and apply itself in different environments. As the field becomes more crowded, we can expect more exciting and innovative developments to emerge.