Sep 26, 2023 ， Reinforcement learning (RL) is a general framework where agents learn to perform actions in an environment so as to maximize a reward.

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Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state.

Deep Q-Learning uses a deep neural network to approximate the different Q-values for each possible action at a state (value-function estimation).

May 19, 2022 ， Deep Q-learning is a powerful tool for creating agents that can solve complex tasks. From Chess to Atari Breakout to FPS games, DQN(Deep Q-learning Networks) ...

Jan 23, 2023 ， Deep Q-Learning is a type of reinforcement learning algorithm that uses a deep neural network to approximate the Q-function.

Apr 11, 2021 ， Deep neural networks are for learning from past experience and RL is for learning via trial and error. Is there possibly a way to learn a function from deep ...

This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v1 task from Gymnasium.

Nov 17, 2020 ， In this tutorial, we'll be sharing a minimal Deep Q-Network implementation (minDQN) meant as a practical guide to help new learners code their own Deep Q- ...

Jan 22, 2021 ， Q-learning is a basic reinforcement learning algorithm. It uses a Q-table to store and update the value of each state-action pair.