QLearning

Decoding the Math behind Q-Learning, Action-Value Functions, and Bellman Equations, and building them from scratch in Python.Image Generated by DALLEIn the previous article, we dipped our toes into the world of reinforcement learning (RL), covering the basics like how agents learn from their surroundings, focusing on a simple setup called GridWorld. We went over the essentials — actions, states, rewards, and how to get around in this environment. If you’re new to this or need a quick recap, it might be a good idea to…

Introduction To Q-Learning Q-Learning is a crucial model-free algorithm in reinforcement learning, focusing on learning the value, or 'Q-value', of actions in specific states. This method excels in environments with unpredictability, as it doesn't need a predefined model of its surroundings. It adapts to stochastic transitions and varied rewards effectively, making it versatile for scenarios where outcomes are uncertain. This flexibility allows Q-Learning to be a powerful tool in scenarios requiring adaptive…

Dive into the world of artificial intelligence — build a deep reinforcement learning gym from scratch.Construct your own Deep Reinforcement Learning Gym — Image by authorTable of ContentsIf you already have a grasp of Reinforcement and Deep Q-Learning concepts, feel free to jump directly to the step-by-step tutorial. There you’ll have all the resources and code necessary to build a Deep Reinforcement Learning gym from the ground up, including the environment, agent, and training protocol.IntroWhy Reinforcement…

Level up your agent to win more difficult games!Continue reading on Towards Data Science » Level up your agent to win more difficult games!Continue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and…

Artificial intelligence (AI) and machine learning (ML) have profoundly impacted a wide range of industries, from healthcare and finance to energy and transportation. Among various AI techniques, reinforcement learning (RL) — a type of machine learning where an agent learns to make decisions by interacting with its environment — has emerged as a potent tool for solving complex, sequential decision-making problems. A significant advancement in RL is the advent of Deep Q-Learning Networks (DQNs), which combine the power of…

A Python implementation of Q-learning to solve the Taxi-v3 environment from OpenAI Gym in an animated Jupyter NotebookPhoto by Alexander Redl on UnsplashThe goal of the Taxi Environment in OpenAI’s Gym — yes, from the company behind ChatGPT and Dall⋅E — is simple and straightforward, making for an excellent introduction to the field of Reinforcement Learning (RL).This article provides a step-to-step guide to implement the environment, learn a policy using tabular Q-learning, and visualize the learned behavior in…

In this article, I will present the reader with the concept of Q-values. For the sake of intuition, the reader can change the Q in Q-values for Quality-values. The q values are numeric values that assign a score for each action taken from each state:Q value functionThe higher the score for a particular action in a given state, the better it is for the agent to take that action.For example, if we can choose from state 1 to go either left or right, then ifQ(left, 1) = 3.187Q(right, 1) = 6.588Then the better action from…

Reinforcement LearningHow to teach an intelligent agent to play simple games using State-Action-Reward-State-Action (SARSA) algorithmImage by bamenny from PixabayThe beauty of Machine Learning is that there is no shortage of approaches for tackling complex tasks. For example, Reinforcement Learning (RL) practitioners have developed multiple algorithms capable of teaching intelligent agents to navigate their environments and perform actions.In this article, I dive deeper into an RL algorithm called SARSA…

Implementation of the Q-Learning algorithm, and application to OpenAI Gym’s Taxi-v3 environmentPhoto by Richard Bell on UnsplashThe first article in this series introduced the basic concepts and components of any Reinforcement Learning system, and explained the theory behind the Q-Learning algorithm. In this article the goal will be to implement the algorithm in Python3, applying it in a real training environment. All the concepts mentioned in the first article (Applied Reinforcement Learning I: Q-Learning) will be…

Photo by Phillip Glickman on UnsplashReinforcement Learning is one of the most intricate fields of Machine Learning, due to its mathematical complexity, as well as the ambitious tasks it tries to achieve.Simply put, the final goal of a RL algorithm is to make an agent capable of acting in an environment as to maximize the total reward.Easier said than done: that single sentence hides a variety of questions, such as:How do I define a “goal” and a “reward”?How do I make an agent “act”?How do I model the “environment”?The…