https://github.com/CLIVERCHEN/Marvel-O2O_Safe_RL
공식 코드Marvel: Accelerating Safe Online Reinforcement Learning with Finetuned Offline Policy
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Safe Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CharlieLeeeee/TQPO
공식 코드Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement Learning
https://github.com/FlowSs/How-to-Certify-Machine-Learning-BasedSafety-critical-Systems-A-Systematic-Literature-Review
공식 코드How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
https://github.com/JingyuanZhou/Safe_RL_Mixed_Traffic
공식 코드Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning
https://github.com/LeCAR-Lab/SafeDPA
공식 코드https://github.com/Mahmoud-Selim/Safe-Reinforcement-Learning-for-Black-Box-Systems-Using-Reachability-Analysis
공식 코드Safe Reinforcement Learning Using Black-Box Reachability Analysis
https://github.com/ManUtdMoon/Safe_MBRL
공식 코드Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
https://github.com/PrajwalKoirala/LSPC-Safe-Offline-RL
공식 코드Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning
https://github.com/ShengrenHou/Energy-management-MIP-Deep-Reinforcement-Learning
공식 코드Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm
https://github.com/UMass-SCALAR-Lab/HC-RLHF
공식 코드Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints
https://github.com/akifumi-wachi-4/safe_near_optimal_mdp
공식 코드Safe Reinforcement Learning in Constrained Markov Decision Processes
https://github.com/akjayant/mbppol
공식 코드https://github.com/alex-yanranwang/awavo
공식 코드Probabilistic Constrained Reinforcement Learning with Formal Interpretability
https://github.com/ammar-n-abbas/CoL-SDRPRL
공식 코드https://github.com/archanabura/dope-doublyoptimisticpessimisticexploration
공식 코드DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
https://github.com/baumanndominik/cme_based_classification_bounds
공식 코드https://github.com/chauncygu/safe-reinforcement-learning-baselines
공식 코드A Review of Safe Reinforcement Learning: Methods, Theory and Applications
https://github.com/cube1324/d-atacom
공식 코드Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
https://github.com/czp16/fcsrl
공식 코드Feasibility Consistent Representation Learning for Safe Reinforcement Learning
https://github.com/data-and-decision-lab/self-improving-RL
공식 코드https://github.com/decisionforce/metadrive
공식 코드MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
https://github.com/evergi/real_validation_saferl_treec_paper
공식 코드https://github.com/geraudnt/rosarl
공식 코드https://github.com/grockious/lcrl
공식 코드https://github.com/gsastry/human-rl
공식 코드Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
https://github.com/gwthomas/safe-mbpo
공식 코드https://github.com/hmhuy0/SIM-RL
공식 코드Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
https://github.com/hnyu/seditor
공식 코드Towards Safe Reinforcement Learning with a Safety Editor Policy
https://github.com/huawei-noah/hebo
공식 코드Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
https://github.com/ilnura/lb_sgd
공식 코드Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
https://github.com/intelligent-control-lab/guard
공식 코드https://github.com/jlwu002/vsrl
공식 코드Verified Safe Reinforcement Learning for Neural Network Dynamic Models
https://github.com/jqueeney/robust-safe-rl
공식 코드Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees
https://github.com/khaoulachehbouni/llama2safeguards
공식 코드From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
https://github.com/lasgroup/lbsgd-rl
공식 코드Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
https://github.com/liqunzhao/a-barrier-lyapunov-actor-critic-reinforcement-learning-approach-for-safe-and-stable-control
공식 코드Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.