Atari Games
226 papers with code • 64 benchmarks • 6 datasets
The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.
( Image credit: Playing Atari with Deep Reinforcement Learning )
Libraries
Use these libraries to find Atari Games models and implementationsDatasets
Most implemented papers
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
Deep Reinforcement Learning with Double Q-learning
The popular Q-learning algorithm is known to overestimate action values under certain conditions.
Prioritized Experience Replay
Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.
Dueling Network Architectures for Deep Reinforcement Learning
In recent years there have been many successes of using deep representations in reinforcement learning.
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.
Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent improvements to the DQN algorithm.
A Distributional Perspective on Reinforcement Learning
We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning.
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x, y) Cartesian space and one-hot pixel space.
Trust Region Policy Optimization
We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement.
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters.