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Reinforcement Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/codelion/optillm

In-context Reinforcement Learning with Algorithm Distillation

https://github.com/coderatwork7/attack

Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

https://github.com/codeslord/RLforATCP

Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

https://github.com/cogitoergoread/rlcard3

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/cognitivemodeling/finn

Weight Uncertainty in Neural Networks

https://github.com/cogsys-tuebingen/uninas

Population Based Training of Neural Networks

https://github.com/col14m/cadrille

cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning

https://github.com/colindbrown/columbia-deep-learning-project

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

https://github.com/colingwuyu/HedgingBox

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/collinarnett/safex_trading_bot

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/collinarnett/xcalibra_trading_bot

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/columbia-robovision/decentralized-multiarm

Learning a Decentralized Multi-arm Motion Planner

https://github.com/conan7882/recurrent-attention-model

Recurrent Models of Visual Attention

https://github.com/cool21th/ai_drug_discovery

ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?

https://github.com/coperception/coperception

Who2com: Collaborative Perception via Learnable Handshake Communication

https://github.com/cor3bit/bertsekas-marl

Multiagent Rollout Algorithms and Reinforcement Learning

https://github.com/core-robotics-lab/icct

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/core-robotics-lab/interpretable_ddts_aistats2020

Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning

https://github.com/coreylowman/ragz

Thinking Fast and Slow with Deep Learning and Tree Search

https://github.com/coreylowman/rl_simply

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/coreylowman/synthesis

Thinking Fast and Slow with Deep Learning and Tree Search

https://github.com/cosmobobak/ttt-ml

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

https://github.com/cosmoquester/2021-dialogue-summary-competition

A Deep Reinforced Model for Abstractive Summarization

https://github.com/cosynus-lix/STAR

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/cove9988/TradingGym

Deep Reinforcement Learning with Double Q-learning

https://github.com/cpark321/bayesian-neural-networks

Weight Uncertainty in Neural Networks

https://github.com/cpark321/uncertainty-deep-learning

Weight Uncertainty in Neural Networks

https://github.com/cpow-89/Augmented-Random-Search

Simple random search provides a competitive approach to reinforcement learning

https://github.com/cpwan/rlor

RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research

https://github.com/cranial-xix/famo

Gradient Surgery for Multi-Task Learning

https://github.com/crashmoon/MAML-Pytorch-Multi-GPUs

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

https://github.com/crazydonkey200/neural-symbolic-machines

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/crazyleg/TD3-reacher

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/createamind/VDB-GAN

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

https://github.com/createamind/softlearning

Soft Actor-Critic Algorithms and Applications

https://github.com/cross32768/Dreamer_PyTorch

Dream to Control: Learning Behaviors by Latent Imagination

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.