Atari Games

283 papers with code • 64 benchmarks • 6 datasets

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

( Image credit: Playing Atari with Deep Reinforcement Learning )


Use these libraries to find Atari Games models and implementations
12 papers
11 papers
7 papers
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Most implemented papers

Playing Atari with Deep Reinforcement Learning

labmlai/annotated_deep_learning_paper_implementations 19 Dec 2013

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

Deep Reinforcement Learning with Double Q-learning

labmlai/annotated_deep_learning_paper_implementations 22 Sep 2015

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

Prioritized Experience Replay

labmlai/annotated_deep_learning_paper_implementations 18 Nov 2015

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

Dueling Network Architectures for Deep Reinforcement Learning

labmlai/annotated_deep_learning_paper_implementations 20 Nov 2015

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

Asynchronous Methods for Deep Reinforcement Learning

ray-project/ray 4 Feb 2016

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

Rainbow: Combining Improvements in Deep Reinforcement Learning

thu-ml/tianshou 6 Oct 2017

The deep reinforcement learning community has made several independent improvements to the DQN algorithm.

Overcoming catastrophic forgetting in neural networks

ContinualAI/avalanche 2 Dec 2016

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence.

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

ray-project/ray 10 Mar 2017

We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients.

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

deepmind/scalable_agent ICML 2018

In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters.

A Distributional Perspective on Reinforcement Learning

facebookresearch/ReAgent ICML 2017

We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning.