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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/ikostrikov/pytorch-rl

Continuous Deep Q-Learning with Model-based Acceleration

https://github.com/ilonatommy/DLR_FacialEmotionRecognition

Image Classification by Reinforcement Learning with Two-State Q-Learning

https://github.com/improbable-ai/eipo

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

https://github.com/intelligent-environments-lab/CityLearn

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/invictos/InsacarDQN

Playing Atari with Deep Reinforcement Learning

https://github.com/isp1tze/MAProj

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

https://github.com/jadag/DDQN_mario

Deep Reinforcement Learning with Double Q-learning

https://github.com/jaem-seo/AI_tokamak_control

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/jakegrigsby/deep_control

Continuous Deep Q-Learning with Model-based Acceleration

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/ddpg.py

Continuous control with deep reinforcement learning

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/sac.py

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

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/td3.py

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/jakub-zarzycki2022/gym-pbn-stac

Deep Reinforcement Learning for Control of Probabilistic Boolean Networks

https://github.com/janhuenermann/neurojs

Increasing the Action Gap: New Operators for Reinforcement Learning

https://github.com/jansenkeith501/CS295-MADDPG

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

https://github.com/jbr-ai-labs/BAROCCO

Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments

https://github.com/jeffchy/Artificial-Idiot

Playing FPS Games with Deep Reinforcement Learning

https://github.com/jeffery1236/Atari_DoubleDeepQNetwork

Deep Reinforcement Learning with Double Q-learning

https://github.com/jezzarax/drlnd_p1_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/jhu-lcsr/good_robot

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

https://github.com/jiayu-ch15/MPE-for-curriculum-learning

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

https://github.com/jihoonerd/Deep-Reinforcement-Learning-with-Double-Q-learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/jingdic/rgmcomm

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

https://github.com/johannesharmse/multi_agent_RL

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

https://github.com/jollyraven100/Quant_algorithmic-trading_and_More

Taming the Noise in Reinforcement Learning via Soft Updates

https://github.com/jollyraven100/Trade-Ideas-and-Research-Reference

Taming the Noise in Reinforcement Learning via Soft Updates

https://github.com/jonaths/dqn-grid

Playing Atari with Deep Reinforcement Learning

https://github.com/jonaths/tf-dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/joshiatul/game_playing

Playing Atari with Deep Reinforcement Learning

https://github.com/junyoung0131/Mol-DQN

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/justinjfu/diagnosing_qlearning

Diagnosing Bottlenecks in Deep Q-learning Algorithms

https://github.com/jvking/text-games

Deep Reinforcement Learning with a Natural Language Action Space

https://github.com/jvoynow/DQN-analysis-with-2048

Deep Reinforcement Learning with Double Q-learning

https://github.com/jyqhahah/rl_maddpg_matd3

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

https://github.com/kairproject/kair_algorithms_draft

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

https://github.com/kargarisaac/macrpo

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.