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

https://github.com/170928/-Review-Proximal-Policy-Optimization-Algorithms

Proximal Policy Optimization Algorithms

https://github.com/1jsingh/rl_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/2023-MindSpore-1/ms-code-221/tree/main/qmix

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/2023-MindSpore-1/ms-code-52

Playing Atari with Deep Reinforcement Learning

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/2796gaurav/code_examples/tree/main/Perceiver

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/2mawi2/master-thesis-experiments

Proximal Policy Optimization Algorithms

https://github.com/4rChon/NL-FuN

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/7starsea/Prioritized-Experience-Replay

Prioritized Experience Replay

https://github.com/ACampero/dopamine

Prioritized Experience Replay

https://github.com/AI-RG/rl-experiments

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/ASzot/ppo-pytorch

Proximal Policy Optimization Algorithms

https://github.com/Abdelhamid-bouzid/Distributional-RL

A Distributional Perspective on Reinforcement Learning

https://github.com/Adeikalam/Go-Explore

Go-Explore: a New Approach for Hard-Exploration Problems

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

Deep Reinforcement Learning with Double Q-learning

https://github.com/Amadeus979/decision-transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/AndrewJWashington/protodriver

Playing Atari with Deep Reinforcement Learning

https://github.com/Angeliki-Panoulia/CallForCode

Scalable agent alignment via reward modeling: a research direction

https://github.com/AnnaNikitaRL/EVA

Fast deep reinforcement learning using online adjustments from the past

https://github.com/AnshMittal1811/AugmentedRandomSearch

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Anshu1245/RL-CourseProject

Playing Atari with Deep Reinforcement Learning

https://github.com/Aravind-11/AI-Gaming

Proximal Policy Optimization Algorithms

https://github.com/Aravind-11/IITM_Saastra

Proximal Policy Optimization Algorithms

https://github.com/Aravind-11/Multi-Agent-RL

Proximal Policy Optimization Algorithms

https://github.com/Arrabonae/openai_DDDQN

Prioritized Experience Replay

https://github.com/AsWali/MineAgentFinal

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

https://github.com/AurelianTactics/dqnclipped_dqnreg_prelim_implementation

Evolving Reinforcement Learning Algorithms

https://github.com/AvivNavon/AuxiLearn

Adapting Auxiliary Losses Using Gradient Similarity

https://github.com/BH4/Deep-Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/PPOAgent.py

Proximal Policy Optimization Algorithms

https://github.com/BY571/DQN-Atari-Agents

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/BY571/FQF-and-Extensions

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

https://github.com/BY571/IQN

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/BY571/Munchausen-RL

Munchausen Reinforcement Learning

https://github.com/BY571/SAC_discrete

Soft Actor-Critic for Discrete Action Settings

https://github.com/Bam4d/GVGAI_GYM

Deep Reinforcement Learning for General Video Game AI

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