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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/YuansongFeng/MadMario

Deep Reinforcement Learning with Double Q-learning

https://github.com/Yutongamber/MADDPG

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

https://github.com/ZainRaza14/Neuro-Evolution

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

https://github.com/ZainRaza14/deepRL

Continuous control with deep reinforcement learning

https://github.com/Zartris/TD3_continuous_control

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ZiyangY/IndProject-RL-in-Supply-chain

Continuous control with deep reinforcement learning

https://github.com/Zorrorulz/MultiAgentDDPG-Tennis

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

https://github.com/abbadka/quadcopter

Continuous control with deep reinforcement learning

https://github.com/abefetterman/hamstir-gym

Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

https://github.com/ac-93/soft-actor-critic

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

https://github.com/adaptive-intelligent-robotics/pga-map-elites

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ai-winter/python_motion_planning

Continuous control with deep reinforcement learning

https://github.com/ailab-pku/rl-framework

Continuous control with deep reinforcement learning

https://github.com/ajaysub110/rl-pytorch

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

https://github.com/aksub99/MolDQN-pytorch

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/akterskii/rlpyt

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

https://github.com/alathiya/RL-Quadcoptor-Flying

Continuous control with deep reinforcement learning

https://github.com/alexsax/testing_rlpyt

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

https://github.com/alfredvc/paac

Playing Atari with Deep Reinforcement Learning

https://github.com/alhabk/SGEE--pytorch

Continuous control with deep reinforcement learning

https://github.com/alisidd/Evolution-Strategies

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/aman-khurana/deep-q-learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/amazon-science/meta-q-learning

Meta-Q-Learning

https://github.com/amirmirzaei79/CartPole-DQN-And-DDQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/andreidi/AC_DDPG_walker

Continuous control with deep reinforcement learning

https://github.com/andrejorsula/drl_grasping

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

https://github.com/anh-nn01/Lunar-Lander-Double-Deep-Q-Networks

Deep Reinforcement Learning with Double Q-learning

https://github.com/anita-hu/TF2-RL

Playing Atari with Deep Reinforcement Learning

https://github.com/anonymous-iclr22/trust-region-in-multi-agent-reinforcement-learning

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

https://github.com/anonymous1234517/code

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/apollopower/DOOM-AI

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

https://github.com/araffin/sbx

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

https://github.com/arnomoonens/yarll

Continuous control with deep reinforcement learning

https://github.com/arrival-ltd/catalyst-rl-tutorial

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/asarav/MIT-Deep-Traffic-Solution

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

https://github.com/ashaaher/Reinforcement-Learning-Project

Addressing Function Approximation Error in Actor-Critic Methods

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