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

Showing 2650 of 625 papers

TitleStatusHype
MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for Generalist Agents0
From Code to Play: Benchmarking Program Search for Games Using Large Language Models0
Conformal Symplectic Optimization for Stable Reinforcement LearningCode2
Decision Transformer vs. Decision Mamba: Analysing the Complexity of Sequential Decision Making in Atari GamesCode0
Why the Agent Made that Decision: Explaining Deep Reinforcement Learning with Vision Masks0
Interpreting the Learned Model in MuZero Planning0
Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic ImplicationsCode0
CALE: Continuous Arcade Learning EnvironmentCode7
Enhancing Chess Reinforcement Learning with Graph RepresentationCode1
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning0
Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 GamesCode0
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents0
Streaming Deep Reinforcement Learning Finally WorksCode3
Transforming Game Play: A Comparative Study of DCQN and DTQN Architectures in Reinforcement Learning0
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards0
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model PretrainingCode1
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games0
Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in GamesCode0
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations0
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods0
Normalization and effective learning rates in reinforcement learning0
Understanding and Diagnosing Deep Reinforcement Learning0
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement LearningCode1
FDQN: A Flexible Deep Q-Network Framework for Game AutomationCode0
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model ScalesCode0
Show:102550
← PrevPage 2 of 25Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GDI-H3(200M frames)Score864Unverified
2GDI-H3Score864Unverified
3GDI-I3(200M frames)Score864Unverified
4GDI-I3Score864Unverified
5Bootstrapped DQNScore855Unverified
6FQFScore854.2Unverified
7R2D2Score837.7Unverified
8Ape-XScore800.9Unverified
9Agent57Score790.4Unverified
10IMPALA (deep)Score787.34Unverified
#ModelMetricClaimedVerifiedStatus
1GDI-I3Score34Unverified
2NoisyNet-DuelingScore34Unverified
3GDI-H3Score34Unverified
4TRPO-hashScore34Unverified
5IQNScore34Unverified
6QR-DQN-1Score34Unverified
7GDI-H3(200M frames)Score34Unverified
8Go-ExploreScore34Unverified
9ASL DDQNScore33.9Unverified
10C51 noopScore33.9Unverified
#ModelMetricClaimedVerifiedStatus
1Agent57Score580,328.14Unverified
2QR-DQN-1Score572,510Unverified
3R2D2Score408,850Unverified
4IMPALA (deep)Score351,200.12Unverified
5Ape-XScore302,391.3Unverified
6A2C + SILScore104,975.6Unverified
7MuZero (Res2 Adam)Score94,906.25Unverified
8DreamerV2Score94,688Unverified
9MuZeroScore72,276Unverified
10DNAScore52,398Unverified
#ModelMetricClaimedVerifiedStatus
1GDI-H3(200M frames)Score1,000,000Unverified
2GDI-H3Score1,000,000Unverified
3Agent57Score999,997.63Unverified
4R2D2Score999,996.7Unverified
5MuZeroScore999,976.52Unverified
6MuZero (Res2 Adam)Score999,659.18Unverified
7GDI-I3Score943,910Unverified
8Ape-XScore392,952.3Unverified
9C51 noopScore266,434Unverified
10Duel noopScore50,254.2Unverified