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 601–625 of 625 papers

TitleStatusHype
The Past and Present of Imitation Learning: A Citation Chain Study—0
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning—0
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions—0
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning—0
Towards automatic construction of multi-network models for heterogeneous multi-task learning—0
Reconstructing Actions To Explain Deep Reinforcement Learning—0
Towards Consistent Performance on Atari using Expert Demonstrations—0
Towards continual learning in medical imaging—0
Towards Control-Centric Representations in Reinforcement Learning from Images—0
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations—0
Towards Practical Credit Assignment for Deep Reinforcement Learning—0
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning—0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GDI-H3(200M frames)Score864—Unverified
2GDI-I3(200M frames)Score864—Unverified
3GDI-I3Score864—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
1QR-DQN-1Score34—Unverified
2IQNScore34—Unverified
3TRPO-hashScore34—Unverified
4NoisyNet-DuelingScore34—Unverified
5Go-ExploreScore34—Unverified
6GDI-I3Score34—Unverified
7GDI-H3(200M frames)Score34—Unverified
8GDI-H3Score34—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