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/molomono/CartPole_Optimized_DDQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics

Continuous control with deep reinforcement learning

https://github.com/moreanp/csro

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

https://github.com/morning9393/HAPPO-HATRPO

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

https://github.com/mpatacchiola/gym-multigoal

Reinforcement Learning with Deep Energy-Based Policies

https://github.com/mrcabo/MachineLearningProject

Sampled Policy Gradient for Learning to Play the Game Agar.io

https://github.com/mxblr/DeepRLHockey

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

https://github.com/nagisazj/idaq_public

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

https://github.com/nandomp/AICollaboratory

Playing Atari with Deep Reinforcement Learning

https://github.com/nathanin/pad

Playing Atari with Deep Reinforcement Learning

https://github.com/natsumeS/analysis

Playing Atari with Deep Reinforcement Learning

https://github.com/nav74neet/ddpg4biped

Continuous control with deep reinforcement learning

https://github.com/nav74neet/ddpg_biped

Continuous control with deep reinforcement learning

https://github.com/nav74neet/rl4biped

Continuous control with deep reinforcement learning

https://github.com/nbatfai/Robopsychology

A disembodied developmental robotic agent called Samu Bátfai

https://github.com/nbatfai/SamuBrain

A disembodied developmental robotic agent called Samu Bátfai

https://github.com/nbatfai/SamuCTuring

A disembodied developmental robotic agent called Samu Bátfai

https://github.com/nbatfai/SamuKnows

A disembodied developmental robotic agent called Samu Bátfai

https://github.com/nbatfai/SamuLife

A disembodied developmental robotic agent called Samu Bátfai

https://github.com/nbatfai/SamuTuring

A disembodied developmental robotic agent called Samu Bátfai

https://github.com/nbatfai/nahshon

A disembodied developmental robotic agent called Samu Bátfai

https://github.com/nbopardi/smb

Deep Reinforcement Learning with Double Q-learning

https://github.com/near32/regym

Playing Atari with Deep Reinforcement Learning

https://github.com/neilsgp/RL-Algorithms

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/neuro-soup/es-torch

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/nicklashansen/svea-vit

Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation

https://github.com/nikhilbarhate99/TD3-PyTorch-BipedalWalker-v2

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/niklasschmitz/DeepQLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/ninja18/AtariDQN

Playing Atari with Deep Reinforcement Learning

https://github.com/nnaisense/pgpelib

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/nolanwinsman/Team-Doom

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

https://github.com/oh-lab/UTE-Uncertainty-aware-Temporal-Extension-

TempoRL: Learning When to Act

https://github.com/ollenilsson19/MAP-Elites-GAPG

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ollenilsson19/PGA-MAP-Elites

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/omkarv/pong-from-pixels

Playing Atari with Deep Reinforcement Learning

https://github.com/openai/maddpg

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.