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/sarahisyoung/rlpyt

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

https://github.com/sayantanauddy/hierarchical_bipedal_controller

Continuous control with deep reinforcement learning

https://github.com/schatty/D4PG-pytorch

Continuous control with deep reinforcement learning

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

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

https://github.com/scottemmons/youngs-cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/sea-snell/implicit-language-q-learning

Offline RL for Natural Language Generation with Implicit Language Q Learning

https://github.com/seacevedo/ReinforcementLearningProjects

Deep Reinforcement Learning with Double Q-learning

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

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

https://github.com/seungjaeryanlee/osim-rl-helper

Continuous control with deep reinforcement learning

https://github.com/seungju-k1m/sac-td3-td7

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

https://github.com/sfujim/BCQ

Benchmarking Batch Deep Reinforcement Learning Algorithms

https://github.com/shahabi8/Deep-Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/shahin-01/vqa-ad

Continuous control with deep reinforcement learning

https://github.com/shakedzy/warehouse

Continuous control with deep reinforcement learning

https://github.com/shakti365/nes

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/sharan-dce/DIAL-switch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

https://github.com/sharan-dce/coordination-pong

Multiagent Cooperation and Competition with Deep Reinforcement Learning

https://github.com/shariqiqbal2810/maddpg-pytorch

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

https://github.com/shariqiqbal2810/multiagent-particle-envs

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

https://github.com/shashwatsaxena571/DRL-navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/shehroze37/Augmented-Deep-Reinforcement-Learning-for-3D-environments

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

https://github.com/shehrum/RL_Continous-Control

Continuous control with deep reinforcement learning

https://github.com/shehrum/RL_Navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/siddharthnishtala/DQN-Trader

Deep reinforcement learning for time series: playing idealized trading games

https://github.com/silvadenisaraujo/OpenAIGym-Taxi-v2

Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

https://github.com/silvialuu/DRL-2018

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/siyuofzhou/DDPG

Continuous control with deep reinforcement learning

https://github.com/sliao-mi-luku/DeepRL-multiple-agents-tennis-udacity-drlnd-p3

Continuous control with deep reinforcement learning

https://github.com/smrjan/robotic-vision

Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods

https://github.com/snu-mllab/edac

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble

https://github.com/somnathhazra/uncertainties_marl

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/songrotek/DDPG

Continuous control with deep reinforcement learning

https://github.com/soumik12345/DDPG

Continuous control with deep reinforcement learning

https://github.com/soumik12345/Twin-Delayed-DDPG

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/souravchk25/alg-game-theory-project

Combining No-regret and Q-learning

https://github.com/sourenaKhanzadeh/snakeAi

Playing Atari with Deep Reinforcement Learning

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