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

https://github.com/Linging/Traffic-Signal-Control

Playing Atari with Deep Reinforcement Learning

https://github.com/LuEE-C/PPO-Keras

Proximal Policy Optimization Algorithms

https://github.com/LukasGardberg/cartpole

Playing Atari with Deep Reinforcement Learning

https://github.com/LukeBolly/dreamerv2

Mastering Atari with Discrete World Models

https://github.com/Lyusungwon/apex_dqn_pytorch

Distributed Prioritized Experience Replay

https://github.com/MEOWMEOW114/nd893-p1-navigation-banana

Deep Reinforcement Learning with Double Q-learning

https://github.com/MOVzeroOne/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/MateuszJanda/netris-ai-robot

Playing Atari with Deep Reinforcement Learning

https://github.com/MathPhysSim/PER-NAF

Prioritized Experience Replay

https://github.com/MatheusMRFM/A3C-LSTM-with-Tensorflow

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/MatteoBrentegani/PPO

Proximal Policy Optimization Algorithms

https://github.com/MaxSobolMark/HardRLWithYoutube

Playing hard exploration games by watching YouTube

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Deep_Reinforcement_Learning_with_Double_Q_learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Playing_Atari_with_Deep_Reinforcement_Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Proximal_Policy_Optimization_Algorithms

Proximal Policy Optimization Algorithms

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Playing Atari with Deep Reinforcement Learning

https://github.com/Miatto-research-group/muzero

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

https://github.com/Michaelrising/Prog-RL

Proximal Policy Optimization Algorithms

https://github.com/MilkSilk/masters_thesis

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/MindCode-4/code-11/tree/main/EWC-Overcoming-catastrophic-forgetting

Overcoming catastrophic forgetting in neural networks

https://github.com/MindCode-4/code-13/tree/main/TRSTMI

Trust Region Policy Optimization

https://github.com/MindCode-4/code-2/tree/main/perceiver

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/MindCode-4/code-3/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/MindCode-4/code-6/tree/main/EWC-Overcoming-catastrophic-forgetting

Overcoming catastrophic forgetting in neural networks

https://github.com/MindCode-4/code-8/tree/main/optimize-coordconv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/MindSpore-scientific-2/code-7/tree/main/SISDTA

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/MindSpore-scientific/code-14/tree/main/TRSTMI

Trust Region Policy Optimization

https://github.com/MindSpore-scientific/code-2/tree/main/TRSTMI

Trust Region Policy Optimization

https://github.com/MindSpore-scientific/code-5/tree/main/optimize-coordconv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/Minhchuyentoancbn/Continual-Learning

Overcoming catastrophic forgetting in neural networks

https://github.com/MohammadAsadolahi/DDQN_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Deep Reinforcement Learning with Double Q-learning

https://github.com/MohammadAsadolahi/Dueling_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/MohammadAsadolahi/Gradient-Free-Reinforcement-Learning-solving-Openai-gym-LunarLanderV2-by-Evolution-Strategy-python/blob/main/README.md

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Montherapy/Deep-reinforcement-learning-for-multi-class-imbalanced-classification

Deep Reinforcement Learning with Double Q-learning

https://github.com/MrDaubinet/collaboration-and-competition

Prioritized Experience Replay

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