https://github.com/liqunzhao/neural-ordinary-differential-equations-based-lyapunov-barrier-actor-critic-nlbac
공식 코드Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations
Papers with Code 아카이브
Safe Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/liuzuxin/cvpo-safe-rl
공식 코드Constrained Variational Policy Optimization for Safe Reinforcement Learning
https://github.com/liuzuxin/dsrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
https://github.com/liuzuxin/fsrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
https://github.com/liuzuxin/osrl
공식 코드Constrained Decision Transformer for Offline Safe Reinforcement Learning
https://github.com/liuzuxin/safe-mbrl
공식 코드Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method
https://github.com/liuzuxin/safe-rl-robustness
공식 코드On the Robustness of Safe Reinforcement Learning under Observational Perturbations
https://github.com/mahaitongdae/Feasible-Actor-Critic
공식 코드Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety
https://github.com/mahaitongdae/Reachability_Constrained_RL
공식 코드https://github.com/mahaitongdae/safe_exp_env
공식 코드Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
https://github.com/metadriverse/metadrive
공식 코드MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
https://github.com/ming93/Safe_reinforcement_learning
공식 코드Convergent Policy Optimization for Safe Reinforcement Learning
https://github.com/nikeke19/safe-mult-rl
공식 코드A Multiplicative Value Function for Safe and Efficient Reinforcement Learning
https://github.com/nolanwagener/safe_rl
공식 코드Safe Reinforcement Learning Using Advantage-Based Intervention
https://github.com/pku-alignment/omnisafe
공식 코드https://github.com/pku-alignment/safe-rlhf
공식 코드https://github.com/pku-alignment/safedreamer
공식 코드https://github.com/redrew/cap
공식 코드Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
https://github.com/rl-boxes/safe-rl
공식 코드CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
https://github.com/rllab-snu/safe-distributional-actor-critic
공식 코드Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
https://github.com/roosephu/crabs
공식 코드https://github.com/sacktock/ambs
공식 코드Approximate Model-Based Shielding for Safe Reinforcement Learning
https://github.com/safe-slac/safe-slac
공식 코드Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
https://github.com/saferl-lab/safety-mujoco
공식 코드https://github.com/sailrit/risk-averse-td-learning
공식 코드Risk-Averse Reinforcement Learning: An Optimal Transport Perspective on Temporal Difference Learning
https://github.com/satchitchatterji/shieldedmarlthesis
공식 코드Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning
https://github.com/sharma1256/cbf-constrained_ppo
공식 코드Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
https://github.com/simonzhan-code/step-wise_saferl_pixel
공식 코드State-Wise Safe Reinforcement Learning With Pixel Observations
https://github.com/sisl/AutomotiveSafeRL
공식 코드Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
https://github.com/team-daniel/advice
공식 코드Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
https://github.com/thuzouwenjun/mupo
공식 코드https://github.com/thxsxth/POMDP_RLSepsis
공식 코드https://github.com/tumcps/commonpower
공식 코드CommonPower: A Framework for Safe Data-Driven Smart Grid Control
https://github.com/utiasDSL/safe-control-gym
공식 코드Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
https://github.com/wleiiiii/gym-atc-attack-project
공식 코드Explainable and Safe Reinforcement Learning for Autonomous Air Mobility
https://github.com/yassinech/caps
공식 코드Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.