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Deep Reinforcement Learning

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

https://github.com/MrSyee/pg-is-all-you-need

Continuous control with deep reinforcement learning

https://github.com/MuditShivendra/Traffic_Light_Control

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/N0r9st/a2c-jax

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/NOHYC/autonomous_driving_car_project

Continuous control with deep reinforcement learning

https://github.com/Narsil/rl-baselines

Exploration by Random Network Distillation

https://github.com/Nasdin/ReinforcementLearning-AtariGame

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/NeekhilD/Learning-Competition

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/NervanaSystems/coach

Playing Atari with Deep Reinforcement Learning

https://github.com/NikolausBerl/Udacity_DRLN_Navigation_Project

Deep Reinforcement Learning with Double Q-learning

https://github.com/OMS1996/Carla_The_RL_Self-Driving-Car

Deep Reinforcement Learning with Double Q-learning

https://github.com/Octavio-Pappalardo/RL2-implementation-pytorch

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

https://github.com/Olloxan/Pytorch-A2C

Imagination-Augmented Agents for Deep Reinforcement Learning

https://github.com/OpenLLMAI/OpenLLaMA2

Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

https://github.com/OpenLLMAI/OpenRLHF

Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

https://github.com/OptimalPandemic/taurus

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/OrthoDex/PCGrad-PyTorch

Gradient Surgery for Multi-Task Learning

https://github.com/PaddlePaddle/PaddleScience

Gradient Surgery for Multi-Task Learning

https://github.com/Panda0406/Adversarial-Learning-Distant-Supervision-RE

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

https://github.com/Panda0406/Reinforcement-Learning-Distant-Supervision-RE

Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

https://github.com/PaulCharnay/Projet_AIF

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Pechckin/MountainCar

Continuous control with deep reinforcement learning

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/Philori22/DDPG-aigym

Continuous control with deep reinforcement learning

https://github.com/Phutoast/Learning2RL

Learning to reinforcement learn

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/R-Sweke/DeepQ-Decoding

Dueling Network Architectures for 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/RL-WFU/multi_agent_attack

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

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