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 351–400 of 625 papers

TitleStatusHype
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors—0
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity—0
Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games—0
Self-Imitation Advantage Learning—0
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning—0
Self-Supervised Structured Representations for Deep Reinforcement Learning—0
A Self-Tuning Actor-Critic Algorithm—0
Shallow Updates for Deep Reinforcement Learning—0
Shared Learning : Enhancing Reinforcement in Q-Ensembles—0
Should I Run Offline Reinforcement Learning or Behavioral Cloning?—0
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning—0
Simultaneously Updating All Persistence Values in Reinforcement Learning—0
Sketch-Based Linear Value Function Approximation—0
Slot Contrastive Networks: A Contrastive Approach for Representing Objects—0
Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL—0
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization—0
Speeding up reinforcement learning by combining attention and agency features—0
State Distribution-aware Sampling for Deep Q-learning—0
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL—0
Strategic Attentive Writer for Learning Macro-Actions—0
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks—0
Striving for Simplicity in Off-Policy Deep Reinforcement Learning—0
Successor-Predecessor Intrinsic Exploration—0
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents—0
CopyCAT: Taking Control of Neural Policies with Constant Attacks—0
Target Entropy Annealing for Discrete Soft Actor-Critic—0
Target Return Optimizer for Multi-Game Decision Transformer—0
Temporal-adaptive Hierarchical Reinforcement Learning—0
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning—0
The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces—0
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
Understanding and Diagnosing Deep Reinforcement Learning—0
Understanding plasticity in neural networks—0
Understanding Visual Concepts with Continuation Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GDI-I3(200M frames)Score864—Unverified
2GDI-H3(200M frames)Score864—Unverified
3GDI-H3Score864—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
1IQNScore34—Unverified
2QR-DQN-1Score34—Unverified
3TRPO-hashScore34—Unverified
4NoisyNet-DuelingScore34—Unverified
5GDI-H3Score34—Unverified
6GDI-H3(200M frames)Score34—Unverified
7GDI-I3Score34—Unverified
8Go-ExploreScore34—Unverified
9Bootstrapped DQNScore33.9—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-H3Score1,000,000—Unverified
2GDI-H3(200M frames)Score1,000,000—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