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/AndrewJWashington/protodriver

Playing Atari with Deep Reinforcement Learning

https://github.com/AnshMittal1811/AugmentedRandomSearch

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Anshu1245/RL-CourseProject

Playing Atari with Deep Reinforcement Learning

https://github.com/Arrabonae/openai_DDDQN

Reinforcement Learning with Dynamic Boltzmann Softmax Updates

https://github.com/AurelianTactics/dqfd-with-keras

Deep Q-learning from Demonstrations

https://github.com/AutumnWu/Streamlined-Off-Policy-Learning

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

https://github.com/BH4/Deep-Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/TD3Agent.py

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/BY571/CQL

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/BY571/Implicit-Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/BY571/Normalized-Advantage-Function-NAF-

Continuous Deep Q-Learning with Model-based Acceleration

https://github.com/BY571/Randomized-Ensembled-Double-Q-learning-REDQ-

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

https://github.com/BY571/Soft-Actor-Critic-and-Extensions

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

https://github.com/Bhaney44/MIT_DeepTraffic

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

https://github.com/Bibyutatsu/Self_Driving_Car

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/BigHeaded2003/ConQUR-Mitigating-Delusional-Bias-in-Deep-Q-Learning

ConQUR: Mitigating Delusional Bias in Deep Q-learning

https://github.com/Brook1711/RIS_components

Continuous control with deep reinforcement learning

https://github.com/CankayaUniversity/ceng-407-408-License-Plate-Recognition-Using-Deep-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/Chan1998/MAAC

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

https://github.com/CharlotteMorrison/Baxter-Research

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/CharlotteMorrison/Baxter-VREP

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/CharlotteMorrison/Baxter-VREP-Version-2

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ChrisProgramming2018/IQL_Lunar_Lander

Deep Inverse Q-learning with Constraints

https://github.com/Codernauti/Exploration-of-DQN-in-CartPole-Environment

Deep Reinforcement Learning with Double Q-learning

https://github.com/Crevass/Hybrid-Agent

Continuous control with deep reinforcement learning

https://github.com/Curt-Park/rainbow-is-all-you-need

Playing Atari with Deep Reinforcement Learning

https://github.com/DLR-RM/stable-baselines3

Playing Atari with Deep Reinforcement Learning

https://github.com/DalBigData/Vessel-RL-Environment

Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments

https://github.com/DanielLSM/safe-rl-tutorial

Continuous control with deep reinforcement learning

https://github.com/DanielTakeshi/DCUR

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/DanielTakeshi/dqn

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

https://github.com/Dekki-Aero/DDPG

Continuous control with deep reinforcement learning

https://github.com/Denbergvanthijs/imbDRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/Denys88/rl_games

Deep Coordination Graphs

https://github.com/DrJupiter/Deep-Reinforcement-Rubiks-Cube-Solver

Revisiting Fundamentals of Experience Replay

https://github.com/Epsilon10/MARL

Reward Machines for Cooperative Multi-Agent Reinforcement Learning

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