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Atari Games

94 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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Latest papers with code

R2D2: Reliable and Repeatable Detector and Descriptor

NeurIPS 2019 naver/r2d2

We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.

ATARI GAMES INTEREST POINT DETECTION KEYPOINT DETECTION METRIC LEARNING

77
01 Dec 2019

Contrastive Learning of Structured World Models

27 Nov 2019tkipf/c-swm

Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.

ATARI GAMES REPRESENTATION LEARNING

148
27 Nov 2019

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

19 Nov 2019johan-gras/MuZero

When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.

ATARI GAMES GAME OF CHESS GAME OF GO GAME OF SHOGI

42
19 Nov 2019

Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy

10 Nov 2019RLMA2019/RLMA

Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples.

ADVERSARIAL ATTACK ATARI GAMES

1
10 Nov 2019

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

2,070
16 Oct 2019

Reusing Convolutional Activations from Frame to Frame to Speed up Training and Inference

2 Sep 2019arnokha/reusing_convolutions

When processing similar frames in succession, we can take advantage of the locality of the convolution operation to reevaluate only portions of the image that changed from the previous frame.

ATARI GAMES TIME SERIES

3
02 Sep 2019

GPU-Accelerated Atari Emulation for Reinforcement Learning

19 Jul 2019NVLABs/cule

We designed and implemented a CUDA port of the Atari Learning Environment (ALE), a system for developing and evaluating deep reinforcement algorithms using Atari games.

ATARI GAMES

107
19 Jul 2019

Striving for Simplicity in Off-Policy Deep Reinforcement Learning

10 Jul 2019google-research/batch_rl

This paper advocates the use of offline (batch) reinforcement learning (RL) to help (1) isolate the contributions of exploitation vs. exploration in off-policy deep RL, (2) improve reproducibility of deep RL research, and (3) facilitate the design of simpler deep RL algorithms.

ATARI GAMES Q-LEARNING

13
10 Jul 2019

Unsupervised State Representation Learning in Atari

NeurIPS 2019 mila-iqia/atari-representation-learning

State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks.

ATARI GAMES REPRESENTATION LEARNING

85
19 Jun 2019

Exploration via Flow-Based Intrinsic Rewards

24 May 2019hellochick/MarioO_O-flow-curioisty

Exploration bonuses derived from the novelty of observations in an environment have become a popular approach to motivate exploration for reinforcement learning (RL) agents in the past few years.

ATARI GAMES OPTICAL FLOW ESTIMATION

83
24 May 2019