Atari Games 100k

6 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

Model-Based Reinforcement Learning for Atari

tensorflow/tensor2tensor 1 Mar 2019

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 8 Apr 2020

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 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

denisyarats/drq ICLR 2021

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

mila-iqia/spr ICLR 2021

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 16 May 2022

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.