Dec 19, 2013 ¡¤ We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.

We present the first deep learning model to successfully learn control policies di- rectly from high-dimensional sensory input using reinforcement learning.

Feb 25, 2015 ¡¤ An artificial agent is developed that learns to play a diverse range of classic Atari 2600 computer games directly from sensory experience, ...

What. They use an implementation of Q-learning (i.e. reinforcement learning) with CNNs to automatically play Atari games. The algorithm receives the raw ...

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.

Oct 2, 2019 ¡¤ Hi Everyone, I recently just completed and open sourced my Pytorch implementation of a Deep Q-Network(DQN) to play Atari Pong.

Jul 7, 2021 ¡¤ A deep learning model that could deal with high dimensional inputs and achieve superhuman benchmarks across different Atari games.

This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement ...

This repository contains an implementation of the Deep Q-Network (DQN) algorithm for playing Atari games.

Oct 30, 2019 ¡¤ 49 networks are trained for 49 games: "A different network was trained on each game: the same network architecture, learning algorithm and hyperparameter ...