Papers with Code 아카이브

Q-Learning

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The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>

코드

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

https://github.com/MrDaubinet/collaboration-and-competition

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

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

Continuous control with deep reinforcement learning

https://github.com/NOHYC/autonomous_driving_car_project

Continuous control with 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/NeuroCSUT/intentions

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

https://github.com/NikolausBerl/Udacity_DRLN_Navigation_Project

Deep Reinforcement Learning with Double Q-learning

https://github.com/NrLabFreiburg/inverse-q-learning

Deep Inverse Q-learning with Constraints

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

Deep Reinforcement Learning with Double Q-learning

https://github.com/Parapompadoo/Temporal_Action_Detection

A Self-Adaptive Proposal Model for Temporal Action Detection based on 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/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/RL-WFU/multi_agent_attack

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

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

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

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

Playing Atari with Deep Reinforcement Learning

https://github.com/RUKip/MachineLearningProject

Sampled Policy Gradient for Learning to Play the Game Agar.io

https://github.com/Rabrg/dqn

Playing Atari with Deep Reinforcement Learning

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

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

https://github.com/RandyDeng/gym_connect4

Playing Atari with Deep Reinforcement Learning

https://github.com/Remtasya/DDPG-Actor-Critic-Reinforcement-Learning-Reacher-Environment

Continuous control with deep reinforcement learning

https://github.com/RevanMacQueen/LearningFromHumans

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

https://github.com/RexGLiu/rlpyt_crbp

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

https://github.com/Ring367/A-Deep-Reinforcement-Learning-Network-for-Traffic-Light-Cycle-Control

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

https://github.com/Ring367/Double-Dueling-DQN-to-control-traffic-lights-on-SUMO

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

https://github.com/RobotMobile/rl-paper-review

Playing Atari with Deep Reinforcement Learning

https://github.com/RojaImmanni/FlappyBird_using_RL

FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques

https://github.com/Roman-Kozachek/TradeBot

Deep Reinforcement Learning with Double Q-learning

https://github.com/RyanHaoranLi/CAR-RL

Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error

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