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/grass123-hub/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/gt-coar/Q-Learning-LFA

Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning

https://github.com/guanton/umfi_python

Information-Theoretic State Variable Selection for Reinforcement Learning

https://github.com/guillaumeboniface/bananaland

Deep Reinforcement Learning with Double Q-learning

https://github.com/guillaumeboniface/reacher

Continuous control with deep reinforcement learning

https://github.com/gwthomas/gtml

Continuous control with deep reinforcement learning

https://github.com/gwthomas/iql-pytorch

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/gznyyb/deep_reinforcement_learning_Pong

Deep Reinforcement Learning with Double Q-learning

https://github.com/h-aboutalebi/SparceReward

Continuous control with deep reinforcement learning

https://github.com/halajadallah/RL-Quadcopter_project

Continuous control with deep reinforcement learning

https://github.com/hamidreza-mazandarani/SAMA-D3QL

A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications

https://github.com/hamishs/JAX-RL

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/han-won/PlayingAtariWithMindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/haosulab/ManiSkill-Learn

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/harvitronix/reinforcement-learning-car

Playing Atari with Deep Reinforcement Learning

https://github.com/hegde95/ViZDoom_with_Sound

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

https://github.com/hemilpanchiwala/Dueling-Network-Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Dueling_Network_Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Hindsight-Experience-Replay

Continuous control with deep reinforcement learning

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

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

https://github.com/hill-a/stable-baselines

Playing Atari with Deep Reinforcement Learning

https://github.com/howuhh/sac-n-jax

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble

https://github.com/hw9603/DQfD-PyTorch

Deep Q-learning from Demonstrations

https://github.com/hyerinshelly/RL_COVID-19_Korea

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

https://github.com/hyunin-lee/ForecasterSAC

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

https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient

Continuous control with deep reinforcement learning

https://github.com/iDataist/Tennis-With-Multi-Agent-Reinforcement

Continuous control with deep reinforcement learning

https://github.com/ianlimle/ItsMeMario

Deep Reinforcement Learning with Double Q-learning

https://github.com/iassael/learning-to-communicate

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

https://github.com/iassael/tax-evasion-torch

Using deep Q-learning to understand the tax evasion behavior of risk-averse firms

https://github.com/icmlanon58443043/vizdoom

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

https://github.com/icmlanon58443043/vizdoomicmlanon

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

https://github.com/ifestus/rl

Deep Reinforcement Learning with Double Q-learning

https://github.com/igoracmorais/inteligencia_artificial

Playing Atari with Deep Reinforcement Learning

https://github.com/ikostrikov/cql-results

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/ikostrikov/jax-rl

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

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