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Reinforcement Learning

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코드

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

https://github.com/ghliu/pytorch-ddpg

Continuous control with deep reinforcement learning

https://github.com/ghostFaceKillah/expert

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/giannifranchi/NAO_morpho

Neural Architecture Optimization

https://github.com/giarcieri/assessing-the-influence-of-models-on-the-performance-of-reinforcement-learning-algorithms

Concrete Dropout

https://github.com/gijskoning/ReproducingCURL

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

https://github.com/gingkg/multiagent-particle-envs

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/gingkg/smac

Counterfactual Multi-Agent Policy Gradients

https://github.com/github-jnauta/pytorch-pne

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

https://github.com/gkassel/pyaixi

A Monte Carlo AIXI Approximation

https://github.com/glambrechts/informed-dreamer

Mastering Diverse Domains through World Models

https://github.com/glassices/sokoban_planner

Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning

https://github.com/glinscott/leela-chess

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

https://github.com/gmh14/IRLAS

IRLAS: Inverse Reinforcement Learning for Architecture Search

https://github.com/gmoss1/Kaggle-Halite-IV-RL

Proximal Policy Optimization Algorithms

https://github.com/goktug97/bohb-hpo

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

https://github.com/goldbattle/snakes_mal

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/gomerudo/nas-dmrl

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/gomerudo/openai-baselines

Learning to reinforcement learn

https://github.com/goncharom/PPOv1

Proximal Policy Optimization Algorithms

https://github.com/goodbyeearth/myVBC

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

https://github.com/goodls-cs/leela-zero

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

https://github.com/google-deepmind/acme

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/google-deepmind/android_env

AndroidEnv: A Reinforcement Learning Platform for Android

https://github.com/google-deepmind/open_spiel

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/google-deepmind/pysc2

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/google-deepmind/scalable_agent

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

https://github.com/google-deepmind/spriteworld

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration

https://github.com/google-deepmind/streetlearn

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

https://github.com/google-research-datasets/RxR

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

https://github.com/google-research/android_world

Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

https://github.com/google-research/dice_rl

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections

https://github.com/google-research/dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/google-research/google-research/tree/master/bisimulation_aaai2020

Dopamine: A Research Framework for Deep Reinforcement Learning

https://github.com/google-research/google-research/tree/master/enas_lm

DARTS: Differentiable Architecture Search

https://github.com/google-research/pisac

DeepMind Control Suite

https://github.com/google-research/tensor2robot/tree/master/research/grasp2vec

Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

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