Papers with Code 아카이브
Policy Gradient Methods
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained
https://github.com/lukemelas/image-paragraph-captioning
https://github.com/magnusja/ppo
https://github.com/manish-pra/trcopo
https://github.com/mark-gluzman/NmodelPPO
https://github.com/marload/DeepRL-TensorFlow2
https://github.com/mazm13/Image-to-Tree.pytorch
https://github.com/mbhenaff/pcpg
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
https://github.com/michael-snower/ppo
https://github.com/microsoft/strategically_efficient_rl
https://github.com/mightypirate1/DRL-Tetris
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/mightypirate1/PPO_homebrew
https://github.com/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore
https://github.com/miroblog/tf_deep_rl_trader
https://github.com/mit-realm/neuriss
https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics
https://github.com/morikatron/GAIL_PPO
https://github.com/morikatron/PPO
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/mykolesiko/nlp_projects
https://github.com/near32/regym
https://github.com/nikhilbarhate99/PPO
https://github.com/nikhilbarhate99/PPO-PyTorch
https://github.com/nitthilan/pommerman
https://github.com/nric/ProximalPolicyOptimizationContinuousKeras
https://github.com/nvlabs/gbrl_sb3
https://github.com/openpsi-projects/srl
https://github.com/pat-coady/trpo
https://github.com/paulorauber/hpg
https://github.com/phixion/phixion
https://github.com/ppocma/ppocma
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/pytorch/rl/tree/main/examples/ppo
https://github.com/quantum-fusion/AI-deeplearning
https://github.com/ray-project/ray/tree/master/rllib
https://github.com/reinforcement-learning-kr/pg_travel
https://github.com/rshnn/battleship
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.