About
The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.
( Image credit: Playing Atari with Deep Reinforcement Learning )
Benchmarks
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Subtasks
Datasets
Greatest papers with code
Deep Exploration via Bootstrapped DQN
NeurIPS 2016
• tensorflow/models
• 
Efficient exploration in complex environments remains a major challenge for reinforcement learning.
Ranked #2 on
Atari Games
on Atari 2600 Freeway
On Catastrophic Interference in Atari 2600 Games
• google-research/google-research
•
Our study provides a clear empirical link between catastrophic interference and sample efficiency in reinforcement learning.
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
• google-research/google-research
•
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.
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.
Implicit Quantile Networks for Distributional Reinforcement Learning
In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.
Ranked #1 on
Atari Games
on Atari 2600 Freeway
Prioritized Experience Replay
• google/dopamine
•
Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.
Ranked #3 on
Atari Games
on Atari 2600 Kangaroo
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
• deepmind/deepmind-research
•
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.
Ranked #6 on
Atari Games
on Atari 2600 Star Gunner
Dueling Network Architectures for Deep Reinforcement Learning
In recent years there have been many successes of using deep representations in reinforcement learning.
Ranked #1 on
Atari Games
on Atari 2600 Pong
Deep Reinforcement Learning with Double Q-learning
The popular Q-learning algorithm is known to overestimate action values under certain conditions.
Ranked #7 on
Atari Games
on Atari 2600 Beam Rider