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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/botforge/simplementation

Deep Reinforcement Learning with Double Q-learning

https://github.com/bowenbaker/metaqnn

Designing Neural Network Architectures using Reinforcement Learning

https://github.com/brain-research/mirage-rl

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

https://github.com/brando130/CSharp_NN

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

https://github.com/buseskorkmaz/fms-at-work

Offline RL for Natural Language Generation with Implicit Language Q Learning

https://github.com/caiyingchun/MolDQN

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/caslab-vt/SARNet

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

https://github.com/cathyhxh/ctds

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/ccolas/rl_stats

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

https://github.com/cesch97/NeuroEvolution

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/chainer/chainerrl

Continuous control with deep reinforcement learning

https://github.com/chandar-lab/RLHive

Playing Atari with Deep Reinforcement Learning

https://github.com/chengyu2/vizdoom_rl_community_canberra

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/choicelab/grasping-invisible

A Deep Learning Approach to Grasping the Invisible

https://github.com/cindycia/Atari-SAC-Discrete

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

https://github.com/claudeHifly/BipedalWalker-v3

Continuous control with deep reinforcement learning

https://github.com/cocolico14/N-step-Dueling-DDQN-PER-Pacman

Deep Reinforcement Learning with Double Q-learning

https://github.com/code-implementation1/Code6/tree/main/ras

Adversarial Learning of a Sampler Based on an Unnormalized Distribution

https://github.com/colindbrown/columbia-deep-learning-project

Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero

https://github.com/core-robotics-lab/icct

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

https://github.com/coreylowman/rl_simply

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/corl-team/CORL

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/cove9988/TradingGym

Deep Reinforcement Learning with Double Q-learning

https://github.com/crazyleg/TD3-reacher

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ctallec/continuous-rl

Making Deep Q-learning methods robust to time discretization

https://github.com/cts198859/deeprl_dist

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/cts198859/deeprl_network

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/cww97/visual-language-grasping

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

https://github.com/cyanrain7/trpo-in-marl

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

https://github.com/cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning

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

https://github.com/cyoon1729/Multi-agent-reinforcement-learning

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

https://github.com/cyrusneary/rm-cooperative-marl

Reward Machines for Cooperative Multi-Agent Reinforcement Learning

https://github.com/czen88/qtrader

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/daggertye/GAN-Q-Learning

https://github.com/dan-lennox/ml-udacity-quadcopter-rl

Continuous control with deep reinforcement learning

https://github.com/danilonumeroso/MEG

Optimization of Molecules via Deep Reinforcement Learning

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