3395 papers with code • 169 benchmarks • 1 datasets

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Use these libraries to find reinforcement-learning models and implementations
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Most implemented papers

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

cbfinn/maml ICML 2017

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.

Prioritized Experience Replay

labmlai/annotated_deep_learning_paper_implementations 18 Nov 2015

Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.

Dueling Network Architectures for Deep Reinforcement Learning

labmlai/annotated_deep_learning_paper_implementations 20 Nov 2015

In recent years there have been many successes of using deep representations in reinforcement learning.

Asynchronous Methods for Deep Reinforcement Learning

ray-project/ray 4 Feb 2016

We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.

Addressing Function Approximation Error in Actor-Critic Methods

sfujim/TD3 ICML 2018

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

ray-project/ray 5 Dec 2017

The game of chess is the most widely-studied domain in the history of artificial intelligence.

DARTS: Differentiable Architecture Search

quark0/darts ICLR 2019

This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner.

Soft Actor-Critic Algorithms and Applications

rail-berkeley/softlearning 13 Dec 2018

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms