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 401–450 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
Scalable Real-Time Recurrent Learning Using Columnar-Constructive NetworksCode0
On the Generalization of Representations in Reinforcement LearningCode0
SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement LearningCode0
Virtual Replay CacheCode0
Optimizing the Neural Architecture of Reinforcement Learning AgentsCode0
Continual Learning In Environments With Polynomial Mixing TimesCode0
Exploring Unknown States with Action BalanceCode0
ExPoSe: Combining State-Based Exploration with Gradient-Based Online SearchCode0
Conservative Optimistic Policy Optimization via Multiple Importance SamplingCode0
Exploration via Flow-Based Intrinsic RewardsCode0
Fast deep reinforcement learning using online adjustments from the pastCode0
Soft Actor-Critic for Discrete Action SettingsCode0
Pay Attention to What and Where? Interpretable Feature Extractor in Vision-based Deep Reinforcement LearningCode0
FDQN: A Flexible Deep Q-Network Framework for Game AutomationCode0
Self-supervised network distillation: an effective approach to exploration in sparse reward environmentsCode0
Formal Language Constraints for Markov Decision ProcessesCode0
Fractal AI: A fragile theory of intelligenceCode0
Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in GamesCode0
Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger AgentsCode0
Performing Deep Recurrent Double Q-Learning for Atari GamesCode0
Unraveling the Rainbow: can value-based methods schedule?Code0
Fully Parameterized Quantile Function for Distributional Reinforcement LearningCode0
ConQUR: Mitigating Delusional Bias in Deep Q-learningCode0
Solving Atari Games Using Fractals And EntropyCode0
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature AttributionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GDI-I3Score864—Unverified
2GDI-H3(200M frames)Score864—Unverified
3GDI-I3(200M frames)Score864—Unverified
4GDI-H3Score864—Unverified
5Bootstrapped DQNScore855—Unverified
6FQFScore854.2—Unverified
7R2D2Score837.7—Unverified
8Ape-XScore800.9—Unverified
9Agent57Score790.4—Unverified
10IMPALA (deep)Score787.34—Unverified
#ModelMetricClaimedVerifiedStatus
1TRPO-hashScore34—Unverified
2QR-DQN-1Score34—Unverified
3NoisyNet-DuelingScore34—Unverified
4IQNScore34—Unverified
5GDI-H3Score34—Unverified
6GDI-H3(200M frames)Score34—Unverified
7GDI-I3Score34—Unverified
8Go-ExploreScore34—Unverified
9C51 noopScore33.9—Unverified
10ASL DDQNScore33.9—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