SOTAVerified

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 625 papers

TitleStatusHype
Training with Worst-Case Distributional Shift causes Overestimation and Inaccuracies in State-Action Value Functions—0
Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation—0
Transforming Game Play: A Comparative Study of DCQN and DTQN Architectures in Reinforcement Learning—0
Transparency and Explanation in Deep Reinforcement Learning Neural Networks—0
Understanding and Diagnosing Deep Reinforcement Learning—0
Understanding plasticity in neural networks—0
Understanding Visual Concepts with Continuation Learning—0
Unlocking the Power of Representations in Long-term Novelty-based Exploration—0
Unsupervised Active Pre-Training for Reinforcement Learning—0
Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning—0
Continual Learning Using World Models for Pseudo-Rehearsal—0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning—0
Value-driven Hindsight Modelling—0
Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm—0
Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning—0
Visual Analogies between Atari Games for Studying Transfer Learning in RL—0
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games—0
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning—0
Visual Rationalizations in Deep Reinforcement Learning for Atari Games—0
WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving—0
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?—0
Why the Agent Made that Decision: Explaining Deep Reinforcement Learning with Vision Masks—0
Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark—0
Contrastive Learning of Structured World ModelsCode0
Unifying Count-Based Exploration and Intrinsic MotivationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GDI-H3(200M frames)Score864—Unverified
2GDI-H3Score864—Unverified
3GDI-I3(200M frames)Score864—Unverified
4GDI-I3Score864—Unverified
5Bootstrapped DQNScore855—Unverified
6FQFScore854.2—Unverified
7R2D2Score837.7—Unverified
8Ape-XScore800.9—Unverified
9Agent57Score790.4—Unverified
10IMPALA (deep)Score787.34—Unverified
#ModelMetricClaimedVerifiedStatus
1GDI-I3Score34—Unverified
2NoisyNet-DuelingScore34—Unverified
3GDI-H3Score34—Unverified
4TRPO-hashScore34—Unverified
5IQNScore34—Unverified
6QR-DQN-1Score34—Unverified
7GDI-H3(200M frames)Score34—Unverified
8Go-ExploreScore34—Unverified
9ASL DDQNScore33.9—Unverified
10C51 noopScore33.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Agent57Score580,328.14—Unverified
2QR-DQN-1Score572,510—Unverified
3R2D2Score408,850—Unverified
4IMPALA (deep)Score351,200.12—Unverified
5Ape-XScore302,391.3—Unverified
6A2C + SILScore104,975.6—Unverified
7MuZero (Res2 Adam)Score94,906.25—Unverified
8DreamerV2Score94,688—Unverified
9MuZeroScore72,276—Unverified
10DNAScore52,398—Unverified
#ModelMetricClaimedVerifiedStatus
1GDI-H3(200M frames)Score1,000,000—Unverified
2GDI-H3Score1,000,000—Unverified
3Agent57Score999,997.63—Unverified
4R2D2Score999,996.7—Unverified
5MuZeroScore999,976.52—Unverified
6MuZero (Res2 Adam)Score999,659.18—Unverified
7GDI-I3Score943,910—Unverified
8Ape-XScore392,952.3—Unverified
9C51 noopScore266,434—Unverified
10Duel noopScore50,254.2—Unverified