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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/PeterJochem/Deep_RL

Continuous control with deep reinforcement learning

https://github.com/PeterJochem/Double_Deep_QLearning

Continuous control with deep reinforcement learning

https://github.com/PettingZoo-Team/PettingZoo

PettingZoo: Gym for Multi-Agent Reinforcement Learning

https://github.com/PhamHuygaco/my_repo

Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping

https://github.com/Philori22/DDPG-aigym

Continuous control with deep reinforcement learning

https://github.com/Phutoast/Learning2RL

Learning to reinforcement learn

https://github.com/Phutoast/Stable-Multi-Agent-Reinforcement-Learning

Learning with Opponent-Learning Awareness

https://github.com/PlaytikaOSS/pybandits

More Robust Doubly Robust Off-policy Evaluation

https://github.com/Pranacahya/FP-GameCerdas

Unity: A General Platform for Intelligent Agents

https://github.com/PriyankaDatar/NLP_Project_Modfications

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/PriyankaDatar/NLP_SQL_Bert

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

https://github.com/PriyankaDatar/NLP_SQL_Project

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/PriyankaDatar/NLP_project_BIDAF

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

https://github.com/Pulkit-Khandelwal/medical-mldg-seg

Learning to Generalize: Meta-Learning for Domain Generalization

https://github.com/Pulkit-Khandelwal/mldg

Learning to Generalize: Meta-Learning for Domain Generalization

https://github.com/PurityFan/bohb_in_nni

BOHB: Robust and Efficient Hyperparameter Optimization at Scale

https://github.com/Qtsho/model-based-rl-navigation

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/QuentinVacher-rl/SoftActorCritic-in-Cpp-using-LibTorch

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

https://github.com/R-Stefano/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/RENHANFEI/patch_sup

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_health_gathering

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_patch_dqn

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_patch_supreme

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RESQUELAB/RL-Teacher-UIAdaptation

Deep reinforcement learning from human preferences

https://github.com/RElbers/info-nce-pytorch

Representation Learning with Contrastive Predictive Coding

https://github.com/RL-Research-Cohiba/Reinforcement_Learning

Goal-directed graph construction using reinforcement learning

https://github.com/RL-WFU/multi_agent_attack

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

https://github.com/RLAgent/state-marginal-matching

Unifying Count-Based Exploration and Intrinsic Motivation

https://github.com/RLeike/connect-four

Playing Atari with Deep Reinforcement Learning

https://github.com/RPSR-DQN/paper-code

Recurrent Predictive State Policy Networks

https://github.com/Rabrg/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/Rafael1s/Deep-Reinforcement-Learning-Udacity

Proximal Policy Optimization Algorithms

https://github.com/RaghavvGoel/marl_transfer_new

Learning Transferable Cooperative Behavior in Multi-Agent Teams

https://github.com/Rajesh-Siraskar/RL_VC_CodeOcean

Reinforcement Learning for Control of Valves

https://github.com/RamiSketcher/decision-transformer-ammi

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/RandyDeng/gym_connect4

Playing Atari with Deep Reinforcement Learning

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