reinforcement-learning
3400 papers with code • 4 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in reinforcement-learning
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Libraries
Use these libraries to find reinforcement-learning models and implementations
NASA C-MAPSS
Most implemented papers
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
cbfinn/maml
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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
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Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
A platform for Applied Reinforcement Learning (Applied RL)
Dueling Network Architectures for Deep Reinforcement Learning
labmlai/annotated_deep_learning_paper_implementations
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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.
Addressing Function Approximation Error in Actor-Critic Methods
sfujim/TD3
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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
The game of chess is the most widely-studied domain in the history of artificial intelligence.
DARTS: Differentiable Architecture Search
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
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A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
