Jan 23, 2023 ， Deep Q-Learning is a variant of Q-Learning that uses a deep neural network to represent the Q-function, rather than a simple table of values.

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

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

May 19, 2022 ， Deep Q-learning is the amalgamation of Reinforcement Learning and Neural Networks. Simple, yet very effective. Deep Q-Learning is a powerful ...

Sep 26, 2023 ， The DQN (Deep Q-Network) algorithm was developed by DeepMind in 2015. It was able to solve a wide range of Atari games (some to superhuman level) ...

Nov 17, 2020 ， Deep Q-Learning uses Experience Replay to learn in small batches in order to avoid skewing the dataset distribution of different states, actions ...

The deep Q-learning algorithm employs a deep neural network to approximate values. It generally works by feeding the initial state into the neural network which ...

In this article, we cover two important concepts in the field of reinforcement learning: Q-learning and deep Q-learning.