Atari Games 100k

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

16 shown of 16 papers with code (18 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
  19 Nov 2019 18 repositories listed Syntology ran 43 of 64 samples · 21 unverified · 62 pointer-only (licence)
  When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.
• Mastering Diverse Domains through World Models
  10 Jan 2023 7 repositories listed Syntology ran 21 of 34 samples · 13 unverified
  Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence.
• CURL: Contrastive Unsupervised Representations for Reinforcement Learning
  8 Apr 2020 7 repositories listed Syntology ran 6 of 8 samples · 2 unverified
  On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features.
• Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
  28 Apr 2020 4 repositories listed Syntology ran 6 of 10 samples · 4 unverified · 8 pointer-only (licence)
  We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or…
• Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
  5 Feb 2024 3 repositories listed Syntology ran 3 of 7 samples · 4 unverified
  We propose HyperAgent, a reinforcement learning (RL) algorithm based on the hypermodel framework for exploration in RL.
• Bigger, Better, Faster: Human-level Atari with human-level efficiency
  30 May 2023 3 repositories listed
  We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark.
• Mastering Atari Games with Limited Data
  30 Oct 2021 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal.
• Transformers are Sample-Efficient World Models
  1 Sep 2022 2 repositories listed Syntology ran 17 of 26 samples · 9 unverified · 26 pointer-only (licence)
  Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems.
• Model-Based Reinforcement Learning for Atari
  1 Mar 2019 2 repositories listed Syntology ran 15 of 21 samples · 6 unverified · 17 pointer-only (licence)
  We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that…
• STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
  14 Oct 2023 1 repository listed Syntology ran 8 of 10 samples · 2 unverified · 10 pointer-only (licence)
  The performance of these algorithms heavily relies on the sequence modeling and generation capabilities of the world model.
• HarmonyDream: Task Harmonization Inside World Models
  30 Sep 2023 1 repository listed Syntology ran 4 of 7 samples · 3 unverified
  Model-based reinforcement learning (MBRL) holds the promise of sample-efficient learning by utilizing a world model, which models how the environment works and typically encompasses components for two tasks: observation…
• On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
  19 Oct 2022 1 repository listed Syntology ran 4 of 13 samples · 9 unverified
  Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so.
• Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning
  22 Sep 2022 1 repository listed
  With this work, we hope to provide some insights into the representations learned by ViT during a self-supervised pretraining with observations from RL environments and which properties arise in the representations that…
• The Primacy Bias in Deep Reinforcement Learning
  16 May 2022 1 repository listed
  This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later.
• Pretraining Representations for Data-Efficient Reinforcement Learning
  9 Jun 2021 1 repository listed Syntology ran 2 of 13 samples · 11 unverified
  Data efficiency is a key challenge for deep reinforcement learning.
• Data-Efficient Reinforcement Learning with Self-Predictive Representations
  12 Jul 2020 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.

Syntology lines on 13 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.