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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/BerkeleyLearnVerify/VerifAI

Proximal Policy Optimization Algorithms

https://github.com/BigHeaded2003/ConQUR-Mitigating-Delusional-Bias-in-Deep-Q-Learning

ConQUR: Mitigating Delusional Bias in Deep Q-learning

https://github.com/Bigpig4396/PyTorch-Soft-Actor-Critic-SAC

Soft Actor-Critic for Discrete Action Settings

https://github.com/Brandon-Rozek/DeepRL

Prioritized Experience Replay

https://github.com/BrianPulfer/PapersReimplementations

Proximal Policy Optimization Algorithms

https://github.com/CSCI4850/S20-team3-project

Prioritized Experience Replay

https://github.com/CSautier/Breakout

Proximal Policy Optimization Algorithms

https://github.com/CSautier/PongBot

Proximal Policy Optimization Algorithms

https://github.com/CVC-Lab/SAC-for-H-Bond-Learning

Mastering Atari with Discrete World Models

https://github.com/CankayaUniversity/ceng-407-408-License-Plate-Recognition-Using-Deep-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/CharlotteMorrison/Baxter-Research

Prioritized Experience Replay

https://github.com/CharlotteMorrison/Baxter-VREP

Prioritized Experience Replay

https://github.com/CharlotteMorrison/Baxter-VREP-Version-2

Prioritized Experience Replay

https://github.com/Clement-Hui/Q-Learning

Prioritized Experience Replay

https://github.com/Coac/CommNet-BiCnet

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

https://github.com/Coac/never-give-up

Never Give Up: Learning Directed Exploration Strategies

https://github.com/Codernauti/Exploration-of-DQN-in-CartPole-Environment

Deep Reinforcement Learning with Double Q-learning

https://github.com/CoffeeddCat/Multiagent_chainMDP

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

https://github.com/ContinualAI/avalanche

Overcoming catastrophic forgetting in neural networks

https://github.com/Crevass/Hybrid-Agent

Proximal Policy Optimization Algorithms

https://github.com/Curt-Park/rainbow-is-all-you-need

Playing Atari with Deep Reinforcement Learning

https://github.com/DDoeuiGongju/MARL_project

RODE: Learning Roles to Decompose Multi-Agent Tasks

https://github.com/DHDev0/Muzero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/DHDev0/Muzero-unplugged

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/DLR-RM/stable-baselines3

Playing Atari with Deep Reinforcement Learning

https://github.com/DMIU-ShELL/MOSAIC

Proximal Policy Optimization Algorithms

https://github.com/Dam930/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/Damcy/prioritized-experience-replay

Prioritized Experience Replay

https://github.com/DanielLSM/safe-rl-tutorial

Safe and Efficient Off-Policy Reinforcement Learning

https://github.com/DanielTakeshi/dqn

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

https://github.com/DanyWind/fastai_bs_finder

An Empirical Model of Large-Batch Training

https://github.com/Dascka/dl_q-learning_comparator

Deep Reinforcement Learning that Matters

https://github.com/DavidCastilloAlvarado/Path-planning-and-Reinforcement-Learning

Proximal Policy Optimization Algorithms

https://github.com/Delavari-Alireza/Decision_Transformer_Sequence-Modeling_Deep_Reinforcement_Learning

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Denbergvanthijs/imbDRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/Denys88/rl_games

The StarCraft Multi-Agent Challenge

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