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Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tensorflow/models
공식 코드https://github.com/tensorflow/tensor2tensor
공식 코드https://github.com/tensorlayer/RLzoo
공식 코드https://github.com/tesfayz/ccm_madrl_mec
공식 코드https://github.com/teslacool/m-curl
공식 코드Masked Contrastive Representation Learning for Reinforcement Learning
https://github.com/tessavdheiden/SCR
공식 코드Social Navigation with Human Empowerment driven Deep Reinforcement Learning
https://github.com/tesslerc/ActionRobustRL
공식 코드Action Robust Reinforcement Learning and Applications in Continuous Control
https://github.com/tgGuo15/PriorImageCaption
공식 코드Improving Reinforcement Learning Based Image Captioning with Natural Language Prior
https://github.com/tgangwani/BMIL
공식 코드Learning Belief Representations for Imitation Learning in POMDPs
https://github.com/tgangwani/GuidanceRewards
공식 코드https://github.com/thaihungle/SAM
공식 코드https://github.com/thanard/me-trpo
공식 코드https://github.com/thanhnguyentang/drbqo
공식 코드On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
https://github.com/thanhnguyentang/mmdrl
공식 코드https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detection
공식 코드Multiple Landmark Detection using Multi-Agent Reinforcement Learning
https://github.com/thayafluss/frp_rl
공식 코드Free Random Projection for In-Context Reinforcement Learning
https://github.com/theilem/uavSim
공식 코드UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
https://github.com/thesmartrobot/ambigym
공식 코드A learning gap between neuroscience and reinforcement learning
https://github.com/thibauttheate/risk-sensitive-policy-with-distributional-reinforcement-learning
공식 코드Risk-Sensitive Policy with Distributional Reinforcement Learning
https://github.com/thinklab-sjtu/eda-ai
공식 코드On Joint Learning for Solving Placement and Routing in Chip Design
https://github.com/thinklab-sjtu/rgm
공식 코드Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
https://github.com/thinkwee/nover
공식 코드NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning
https://github.com/thkreutz/crowdorchestrationsim
공식 코드Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics
https://github.com/thomasbi1/cyberrunner
공식 코드https://github.com/thomasdengiz/multiobjective_localsearch_dsm
공식 코드https://github.com/thomasmiconi/meta-task-generator
공식 코드https://github.com/thomasrochefortb/sogym
공식 코드https://github.com/thomasychen/LOTaD
공식 코드https://github.com/thu-bpm/gradlre
공식 코드Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
https://github.com/thu-keg/verif
공식 코드VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
https://github.com/thu-ml/CEURL
공식 코드PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
https://github.com/thu-ml/fpovi
공식 코드Function Space Particle Optimization for Bayesian Neural Networks
https://github.com/thu-ml/tianshou
공식 코드Tianshou: a Highly Modularized Deep Reinforcement Learning Library
https://github.com/thu-rllab/CFCQL
공식 코드Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
https://github.com/thu-rllab/LESR
공식 코드LLM-Empowered State Representation for Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.