Nov 20, 2017 ¡¤ The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research.

This paper presents results from work reproducing the results of the DQN paper, and highlights key areas in the implementation that were not covered in great ...

I modified the example found from https://keon.io/deep-q-learning/ by implementing the CNN model, target model logic, and frame averaging (among other things).

Feb 28, 2024 ¡¤ A network learning that relationship is called a Deep Q-Network. The network uses the state as input and infers the value of each possible action.

Jun 15, 2024 ¡¤ In this blog post, I'll share the challenges I faced and what I learned as a beginner, together with some tactical tips for debugging DQN on Atari Pong / ...

Jan 19, 2023 ¡¤ Define the Q-network: The Q-network is a deep neural network that takes in the current state of the agent and outputs the Q-values for each ...

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

In this post, we implement the original DQN algorithm from Mnih, V., Kavukcuoglu, K., Silver, D. et al. Human-level control through deep reinforcement learning.

May 5, 2018 ¡¤ The OpenAI Gym toolkit provides a set of physical simulation environments, games, and robot simulators that we can play with and design reinforcement learning ...

The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research.