Atari Games

120 papers with code · Playing Games
Subtask of Video Games

The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.

( Image credit: Playing Atari with Deep Reinforcement Learning )

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Greatest papers with code

Deep Exploration via Bootstrapped DQN

NeurIPS 2016 tensorflow/models

Efficient exploration in complex environments remains a major challenge for reinforcement learning.

ATARI GAMES EFFICIENT EXPLORATION

Implicit Quantile Networks for Distributional Reinforcement Learning

ICML 2018 google/dopamine

In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.

ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING

Prioritized Experience Replay

18 Nov 2015google/dopamine

Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.

ATARI GAMES

Asynchronous Methods for Deep Reinforcement Learning

4 Feb 2016tensorpack/tensorpack

We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.

ATARI GAMES

Dueling Network Architectures for Deep Reinforcement Learning

20 Nov 2015tensorpack/tensorpack

In recent years there have been many successes of using deep representations in reinforcement learning.

ATARI GAMES

Deep Reinforcement Learning with Double Q-learning

22 Sep 2015tensorpack/tensorpack

The popular Q-learning algorithm is known to overestimate action values under certain conditions.

ATARI GAMES Q-LEARNING

Playing Atari with Deep Reinforcement Learning

19 Dec 2013tensorpack/tensorpack

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

ATARI GAMES Q-LEARNING

Increasing the Action Gap: New Operators for Reinforcement Learning

15 Dec 2015janhuenermann/neurojs

Extending the idea of a locally consistent operator, we then derive sufficient conditions for an operator to preserve optimality, leading to a family of operators which includes our consistent Bellman operator.

ATARI GAMES Q-LEARNING

Soft Actor-Critic for Discrete Action Settings

16 Oct 2019p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

Soft Actor-Critic is a state-of-the-art reinforcement learning algorithm for continuous action settings that is not applicable to discrete action settings.

ATARI GAMES

Distributional Reinforcement Learning with Quantile Regression

27 Oct 2017facebookresearch/ReAgent

In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean.

ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING