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Atari Games

83 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.

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Latest papers with code

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

88
19 Jul 2019

Striving for Simplicity in Off-policy Deep Reinforcement Learning

10 Jul 2019google-research/batch_rl

Second, how much of the benefits of recent distributional RL algorithms is attributed to improvements in exploration versus exploitation behavior?

ATARI GAMES Q-LEARNING

1
10 Jul 2019

Unsupervised State Representation Learning in Atari

19 Jun 2019ankeshanand/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

41
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

Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments

12 Apr 2019kclary/variability-RL

Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itself have led researchers to report the performance of learned agents using aggregate metrics of performance over multiple random seeds for a single environment.

ATARI GAMES

3
12 Apr 2019

Deep Policies for Width-Based Planning in Pixel Domains

12 Apr 2019aig-upf/pi-IW

Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner.

ATARI GAMES

2
12 Apr 2019

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

7 Mar 2019kenjyoung/MinAtar

With the representation learning problem simplified, we can perform experiments with significantly less computational expense.

ATARI GAMES REPRESENTATION LEARNING

37
07 Mar 2019

Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning

4 Feb 2019dazcona/obstacletower

Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment.

ATARI GAMES BOARD GAMES

0
04 Feb 2019