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Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the ...
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People also ask
What is the difference between Q-learning and reinforcement learning?
Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action. Q-learning is a type of reinforcement learning. With reinforcement learning, a machine learning model is trained to mimic the way animals or children learn.
What is Q-learning reinforcement learning problem?
Q-learning is a reinforcement learning algorithm that finds an optimal action-selection policy for any finite Markov decision process (MDP). It helps an agent learn to maximize the total reward over time through repeated interactions with the environment, even when the model of that environment is not known.
May 15, 2024
What is deep Q-learning in reinforcement learning?
It aims to enable agents to learn optimal actions in complex, high-dimensional environments. By using a neural network to approximate the Q-function, which estimates the expected cumulative reward for each action in a given state, Deep Q-Learning can handle environments with large state spaces.
What does Q represent in reinforcement learning?
Q-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy. "Q" refers to the function that the algorithm computes – the expected rewards for an action taken in a given state.
May 26, 2022 · This is known as reinforcement learning, and it can be mirrored in machine learning to train a system to perform the desired action on command.
Nov 27, 2020 · A Visual Guide to how and why the Q Learning Algorithm works, in Plain English.
May 4, 2019 · I have a fairly simple game in which I wish to use Q-learning to train an agent, but I have some questions regarding state representation. I'm ...
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Nov 13, 2009 · When using Q-Learning, a bit like Neural Networks, I must make distinction between a learning phase and a using phase? · I read somewhere that it ...
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Feb 14, 2024 · Q-learning is a model-free off-policy reinforcement learning algorithm where the agent (the AI) uses a TD learning approach to train its value- ...
Can you limit the number of actions when using q learning?
stackoverflow.com › questions › can-you...
May 26, 2022 · TL;DR: It is absolutely okay to restrict actions. The available actions can be state-dependent. This can be given by physical limitations ...
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May 15, 2024 · Q-learning is a fascinating and widely used reinforcement learning type with applications ranging from robotics to video game AI.
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