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 451–500 of 625 papers

TitleStatusHype
Generalized Data Distribution Iteration—0
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence—0
GMAC: A Distributional Perspective on Actor-Critic Framework—0
Gradient Monitored Reinforcement Learning—0
GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal—0
Group-Agent Reinforcement Learning with Heterogeneous Agents—0
High Performance Across Two Atari Paddle Games Using the Same Perceptual Control Architecture Without Training—0
Machine versus Human Attention in Deep Reinforcement Learning Tasks—0
Improving Experience Replay with Successor Representation—0
Improving On-policy Learning with Statistical Reward Accumulation—0
Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons—0
Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise—0
Improving width-based planning with compact policies—0
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem—0
In Hindsight: A Smooth Reward for Steady Exploration—0
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents—0
Interpreting the Learned Model in MuZero Planning—0
Investigating Recurrence and Eligibility Traces in Deep Q-Networks—0
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning—0
KF-LAX: Kronecker-factored curvature estimation for control variate optimization in reinforcement learning—0
Latent forward model for Real-time Strategy game planning with incomplete information—0
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act—0
Automatic Reward Shaping from Confounded Offline Data—0
Learning Abstract Models for Long-Horizon Exploration—0
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer—0
Learning Actions and Control of Focus of Attention with a Log-Polar-like Sensor—0
Learning and Querying Fast Generative Models for Reinforcement Learning—0
Learning Dialog Policies from Weak Demonstrations—0
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning—0
Learning Efficient Planning-based Rewards for Imitation Learning—0
Learning Finite State Representations of Recurrent Policy Networks—0
Learning Key Steps to Attack Deep Reinforcement Learning Agents—0
Learning objects from pixels—0
Learning Self-Game-Play Agents for Combinatorial Optimization Problems—0
Learning Shared Dynamics with Meta-World Models—0
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning—0
Learning To Explore With Predictive World Model Via Self-Supervised Learning—0
Learning to play slot cars and Atari 2600 games in just minutes—0
Learning to predict where to look in interactive environments using deep recurrent q-learning—0
Learning to Represent Action Values as a Hypergraph on the Action Vertices—0
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search—0
Learning values across many orders of magnitude—0
LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games—0
Leveraging the Variance of Return Sequences for Exploration Policy—0
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning—0
The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function—0
Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning—0
Loss of Plasticity in Continual Deep Reinforcement Learning—0
Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing—0
Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks—0
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Benchmark Results

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