Q-Learning
Q-learning is a fundamental algorithm in reinforcement learning that enables an agent to learn optimal actions through interaction with its environment. By utilizing a Q-table, which maps states to action values, the agent can determine the best action to take in any given state to maximize cumulative rewards. The learning process involves updating the Q-values based on the rewards received after taking actions, allowing the agent to refine its policy over time. Q-learning is particularly effective for problems with discrete state and action spaces, making it a popular choice for various applications, including game playing and robotics.
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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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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Simple Reinforcement Learning: Q-learning
One of my favorite algorithms that I learned while taking a reinforcement learning course was q-learning. Probably because it was the easiest for me to understand and code, but also because it seemed…...
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