Q-Learning
Q-learning is a widely used reinforcement learning algorithm that enables an agent to learn optimal actions through interaction with its environment. It operates on the principle of learning a policy that maximizes cumulative rewards by estimating the value of taking specific actions in given states. The algorithm utilizes a Q-table to store these values, which are updated iteratively based on the agent’s experiences. Q-learning is particularly effective for problems where the environment can be modeled as a Markov Decision Process, making it suitable for various applications, including game playing and robotics. Its off-policy nature allows for flexibility in learning from different strategies.
Q-Learning
Q-learning is one of the most popular Reinforcement learning algorithms and lends itself much more readily for learning through implementation of toy problems as opposed to scouting through loads of…
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Q-Learning
In the previous section, we discussed the Value Iteration algorithm which requires accessing the complete Markov decision process (MDP), e.g., the transition and reward functions. In this section, we ...
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Cracking Q-Learning
Not a Medium member yet? No worries, you can still read it here ! Last time, we learned the concept of Temporal Difference (TD) learning and explored our first method: SARSA (On-Policy) . This time, w...
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Table-Based Q-Learning in Under 1KB
Q-learning is an algorithm in which an agent interacts with its environment and collects rewards for taking desirable actions. The simplest implementation of Q-learning is referred to as tabular or…
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Q Learning — Deep Reinforcement Learning
Q Learning — Deep Reinforcement Learning Playing Ping Pong Atari Game Table of Contents 1. Problem Statement 2. Value Functions 3. Q Learning 3.a. Theory 3.b. Code 3.c. Problems with Q Learning 4. V ...
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Q-learning for beginners
Train an AI to solve the Frozen Lake environment Image by author The goal of this article is to teach an AI how to solve the ❄️Frozen Lake environment using reinforcement learning. Instead of reading...
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Reinforcement Learning From Scratch: Deep Q-Networks
In reinforcement learning (RL), Q-learning is a foundational algorithm that helps an agent navigate its environment by learning a policy to maximize cumulative rewards…
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Q-Learning is the most basic form of Reinforcement Learning, which doesn’t take advantage of any…
Q-Learning is the most basic form of Reinforcement Learning, which doesn’t take advantage of any neural network but instead uses Q-table to find the best possible action to take at a given state. A…
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Intro to Reinforcement Learning: Q-Learning 101
Q-Learning was first introduced in 1989 by Christopher Watkins as an extension of the dynamic programming paradigm. Q-learning also served as the basis for some of the tremendous achievements of deep…...
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Interactive Q learning
While going through the process of understanding Q learning, I was always fascinated by the grid world (the 2D world made of boxes, where agent moves from one box to another and collect rewards)…
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Double Q-Learning the Easy Way
Update: The best way of learning and practicing Reinforcement Learning is by going to http://rl-lab.com Q-learning (Watkins, 1989) is considered one of the breakthroughs in TD control reinforcement…
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Unlocking the Power of the Q-Learning Algorithm
Let’s have a look at deep Q-learning, that is, the algorithm employed in the DeepMind system to play Atari 2600 games at expert human levels. A basic understanding of how Q-learning works is a…
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