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 301–350 of 625 papers

TitleStatusHype
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
Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning—0
Reinforcement Learning and its Connections with Neuroscience and Psychology—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Reward Prediction Error as an Exploration Objective in Deep RL—0
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals—0
Regulatory Focus: Promotion and Prevention Inclinations in Policy Search—0
Reinforcement Learning and Video Games—0
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards—0
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
Reinforcement Learning with Attention that Works: A Self-Supervised Approach—0
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions—0
Return-Based Contrastive Representation Learning for Reinforcement Learning—0
Return-based Scaling: Yet Another Normalisation Trick for Deep RL—0
Reuse of Neural Modules for General Video Game Playing—0
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning—0
Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks—0
Sample Efficient Deep Neuroevolution in Low Dimensional Latent Space—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