Atari Games 100k
6 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Atari Games 100k
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Atari 100k
Most implemented papers
Model-Based Reinforcement Learning for Atari
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 yields the best results in our setting.
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
MishaLaskin/curl
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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.
Mastering Atari Games with Limited Data
werner-duvaud/muzero-general
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NeurIPS 2021
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.
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
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 pre-training.
Data-Efficient Reinforcement Learning with Self-Predictive Representations
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.
The Primacy Bias in Deep Reinforcement Learning
evgenii-nikishin/rl_with_resets
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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.
