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 501–550 of 625 papers

TitleStatusHype
Maximizing Ensemble Diversity in Deep Reinforcement Learning—0
Maximum Entropy Monte-Carlo Planning—0
Measuring Progress in Deep Reinforcement Learning Sample Efficiency—0
Learning to Constrain Policy Optimization with Virtual Trust Region—0
Metaoptimization on a Distributed System for Deep Reinforcement Learning—0
Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control—0
Methodical Advice Collection and Reuse in Deep Reinforcement Learning—0
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals—0
Mimicking actions is a good strategy for beginners: Fast Reinforcement Learning with Expert Action Sequences—0
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Model Based Reinforcement Learning for Atari—0
Model-Free Episodic Control with State Aggregation—0
Modularization of End-to-End Learning: Case Study in Arcade Games—0
Momentum in Reinforcement Learning—0
Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks—0
MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for Generalist Agents—0
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
Multiplayer Support for the Arcade Learning Environment—0
Natural Value Approximators: Learning when to Trust Past Estimates—0
Parallel Exploration via Negatively Correlated Search—0
Neural Policy Style Transfer—0
Neurohex: A Deep Q-learning Hex Agent—0
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events—0
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Non-Robust Feature Mapping in Deep Reinforcement Learning—0
No-Regret Exploration in Goal-Oriented Reinforcement Learning—0
Normalization and effective learning rates in reinforcement learning—0
Nuclear Norm Maximization Based Curiosity-Driven Learning—0
Object-sensitive Deep Reinforcement Learning—0
Objects matter: object-centric world models improve reinforcement learning in visually complex environments—0
Observe and Look Further: Achieving Consistent Performance on Atari—0
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints—0
Off-Policy Actor-Critic with Shared Experience Replay—0
On Bonus Based Exploration Methods In The Arcade Learning Environment—0
On Bonus-Based Exploration Methods in the Arcade Learning Environment—0
On Effective Parallelization of Monte Carlo Tree Search—0
On Improving Deep Reinforcement Learning for POMDPs—0
Online Meta-learning by Parallel Algorithm Competition—0
On the Role of Weight Sharing During Deep Option Learning—0
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning—0
P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization—0
Perturbation-based exploration methods in deep reinforcement learning—0
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods—0
Playing Atari Ball Games with Hierarchical Reinforcement Learning—0
Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus—0
Policy Optimization with Model-based Explorations—0
Population-Guided Imitation Learning—0
Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement 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