Papers with Code 아카이브

Q-Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

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/JuliaPOMDP/DeepQLearning.jl

Playing Atari with Deep Reinforcement Learning

https://github.com/JustinStitt/acrobotDDQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/K-tang-mkv/baseRLAlgorithm

Playing Atari with Deep Reinforcement Learning

https://github.com/KAIST-AILab/deeprl_practice_colab

Continuous control with deep reinforcement learning

https://github.com/Kaixhin/imitation-learning

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

https://github.com/KatyNTsachi/Hierarchical-RL

Playing Atari with Deep Reinforcement Learning

https://github.com/KavindaKottege/DeepQ-Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/KelvinYang0320/deepbots-panda

Continuous control with deep reinforcement learning

https://github.com/Kikumu/Reinforcement-Learning-repo

Continuous control with deep reinforcement learning

https://github.com/KyleChen400/patch_dqn

Playing FPS Games with Deep Reinforcement Learning

https://github.com/LM095/DDPG-implementation

Continuous control with deep reinforcement learning

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

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

https://github.com/LilTwo/DRL-using-PyTorch

Deep Q-learning from Demonstrations

https://github.com/Linging/Traffic-Signal-Control

Playing Atari with Deep Reinforcement Learning

https://github.com/LucasAlegre/sac-plus

Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

https://github.com/LukasGardberg/cartpole

Playing Atari with Deep Reinforcement Learning

https://github.com/MEOWMEOW114/nd893-p1-navigation-banana

Deep Reinforcement Learning with Double Q-learning

https://github.com/MLCS-Yonsei/ddpg-control

Continuous control with deep reinforcement learning

https://github.com/MLforHealth/rl_representations

Benchmarking Batch Deep Reinforcement Learning Algorithms

https://github.com/MOVzeroOne/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/Manchery/iql-pytorch

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/MarsEleven/car_racer_RL

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

https://github.com/MateuszJanda/netris-ai-robot

Playing Atari with Deep Reinforcement Learning

https://github.com/MathPhysSim/PER-NAF

Continuous control with deep reinforcement learning

https://github.com/MatthieuSarkis/Portfolio-Optimization-and-Goal-Based-Investment-with-Reinforcement-Learning

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

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Deep_Reinforcement_Learning_with_Double_Q_learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Playing_Atari_with_Deep_Reinforcement_Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/Medabid1/RL_Project

Continuous control with deep reinforcement learning

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Playing Atari with Deep Reinforcement Learning

https://github.com/MikulasZelinka/pyfiction

Deep Reinforcement Learning with a Natural Language Action Space

https://github.com/MilkSilk/masters_thesis

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/MohammadAsadolahi/DDQN_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Deep Reinforcement Learning with Double Q-learning

https://github.com/MohammadAsadolahi/Gradient-Free-Reinforcement-Learning-solving-Openai-gym-LunarLanderV2-by-Evolution-Strategy-python/blob/main/README.md

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/MohammadrezaNakhaei/FQL

Flow Q-Learning

https://github.com/Montherapy/Deep-reinforcement-learning-for-multi-class-imbalanced-classification

Deep Reinforcement Learning with Double Q-learning

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