Atari 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 )
Leaderboards
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Latest papers with code
R2D2: Reliable and Repeatable Detector and Descriptor
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
Contrastive Learning of Structured World Models
• tkipf/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.
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
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.
SOTA for Atari Games on Atari 2600 Robotank
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy
• RLMA2019/RLMA
• 
Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples.
Soft Actor-Critic for Discrete Action Settings
• p-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.
#17 best model for
Atari Games
on Atari 2600 Pong
Reusing Convolutional Activations from Frame to Frame to Speed up Training and Inference
• arnokha/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.
GPU-Accelerated Atari Emulation for Reinforcement Learning
• NVLABs/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.
Striving for Simplicity in Off-Policy Deep Reinforcement Learning
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.
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.
Exploration via Flow-Based Intrinsic Rewards
• hellochick/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.