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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/ermongroup/CalibratedModelBasedRL

Calibrated Model-Based Deep Reinforcement Learning

https://github.com/ermongroup/EfficientObjectDetection

Efficient Object Detection in Large Images using Deep Reinforcement Learning

https://github.com/ermongroup/multiagent-gail

Multi-Agent Generative Adversarial Imitation Learning

https://github.com/erwincoumans/tds_experimental

Interactive Differentiable Simulation

https://github.com/esmeralday/MARL

Playing Atari with Deep Reinforcement Learning

https://github.com/esparza83/esparza83-udacity_rl_project3

Emergent Tool Use From Multi-Agent Autocurricula

https://github.com/eth-pbl/elmap-rl-controller

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

https://github.com/ethancaballero/neural-engineers-first-attempt

Hindsight Experience Replay

https://github.com/ethanluoyc/transporter-pytorch

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/ethanmclark1/carla_aebs

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/ethz-asl/rl-navigation

Constrained Policy Optimization

https://github.com/eublefar/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/eugenevinitsky/cdc_bottlenecks

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/evaboost/evaboost

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/evangravelle/AI-projects

Thinking Fast and Slow with Deep Learning and Tree Search

https://github.com/evieq01/oodil

Proximal Policy Optimization Algorithms

https://github.com/evzissel/expgen

Decoupling Value and Policy for Generalization in Reinforcement Learning

https://github.com/ewrfcas/Reinforced-Mnemonic-Reader

Reinforced Mnemonic Reader for Machine Reading Comprehension

https://github.com/ex4sperans/freesound-classification

Representation Learning with Contrastive Predictive Coding

https://github.com/eyounx/virtualtaobao

Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning

https://github.com/fabian57fabian/MinGrid-Improved-RL-Methods

Accuracy-based Curriculum Learning in Deep Reinforcement Learning

https://github.com/fabiopardo/qmap

Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

https://github.com/fabricerosay/AlphaGPU

Monte-Carlo Tree Search as Regularized Policy Optimization

https://github.com/facebookarchive/MazeBase

MazeBase: A Sandbox for Learning from Games

https://github.com/facebookresearch/CPC_audio

Representation Learning with Contrastive Predictive Coding

https://github.com/facebookresearch/DomainBed

Learning to Generalize: Meta-Learning for Domain Generalization

https://github.com/facebookresearch/EgoTV

AI2-THOR: An Interactive 3D Environment for Visual AI

https://github.com/facebookresearch/EmbodiedQA

Embodied Question Answering

https://github.com/facebookresearch/Hanabi_SPARTA

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

https://github.com/facebookresearch/Horizon

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/ParlAI

ParlAI: A Dialog Research Software Platform

https://github.com/facebookresearch/RTFM

RTFM: Generalising to Novel Environment Dynamics via Reading

https://github.com/facebookresearch/ReAgent

Deep Reinforcement Learning with Double Q-learning

https://github.com/facebookresearch/ddr

Decoupling Dynamics and Reward for Transfer Learning

https://github.com/facebookresearch/denoised_mdp

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/facebookresearch/diplomacy_cicero

Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning

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