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/ruotianluo/ImageCaptioning.pytorch
https://github.com/ruotianluo/Transformer_Captioning
https://github.com/ruotianluo/neuraltalk2.pytorch
https://github.com/ruotianluo/self-critical.pytorch
https://github.com/s-52/ImageCaptions
https://github.com/s-sd/task-amenability
https://github.com/saschaschramm/Pong
https://github.com/sc2crazy/StarCrackRL
https://github.com/seungjaeryanlee/osim-rl-helper
https://github.com/sgondala/GoogleConceptualCaptioning
https://github.com/shiningsunnyday/mcts-chess
https://github.com/shreyesss/PPO-implementation-keras-tensorflow
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/shuishida/soaprl
https://github.com/siddharthverma314/proximalpolicyoptimization
https://github.com/silvialuu/DRL-2018
https://github.com/sirakik/mprg_fc
https://github.com/sirakik/ppo_football
https://github.com/sirdr/TRPO.jl
https://github.com/smj007/Breakout_A3C
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/sparsh-ai/RecommenderSystems
Ekar: An Explainable Method for Knowledge Aware Recommendation
https://github.com/spideralessio/TRPO-Tensorflow2
https://github.com/supratikp/HOOF
https://github.com/synthlabsai/big-math
https://github.com/taku-y/20181125-pybullet
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/takuseno/ppo
https://github.com/tatsu-lab/linguistic_calibration
https://github.com/tcmxx/CNTKUnityTools
https://github.com/tedmoskovitz/constrainedrl4lms
https://github.com/tensorlayer/RLzoo
https://github.com/tfolkman/deep-learning-experiments
https://github.com/theresearchai/vehicle_routing_rl_2
https://github.com/tidedra/vl-rlhf
https://github.com/tilkb/thermoai
https://github.com/tmjeong1103/RL_with_RAY
https://github.com/tobiasemrich/SchafkopfRL
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.