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Safe Reinforcement Learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/yemam3/mod-rl-rcbf

공식 코드

Safe Reinforcement Learning Using Robust Control Barrier Functions

https://github.com/yihangyao/OASIS

공식 코드

OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning

https://github.com/yingchengyang/CPPO

공식 코드

Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk

https://github.com/zgong11/TraC

공식 코드

Offline Safe Reinforcement Learning Using Trajectory Classification

https://github.com/zifanwu/cal

공식 코드

Off-Policy Primal-Dual Safe Reinforcement Learning

https://github.com/zlr20/saferl_kit

공식 코드

SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving

https://github.com/zmsn-2077/cup-safe-rl

공식 코드

Constrained Update Projection Approach to Safe Policy Optimization

https://github.com/zuzuba/CISR_NeurIPS20

공식 코드

Safe Reinforcement Learning via Curriculum Induction

https://gitlab.com/mit-mers/safe-visual-mapf

공식 코드

Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning

https://gitlab.com/wenlu057/traffic-safety

공식 코드

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

https://github.com/62subinh/deeprl_safety_specification

Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach

https://github.com/Bigpig4396/PyTorch-Constrained-Policy-Optimization-CPO

Constrained Policy Optimization

https://github.com/DanielLSM/safe-rl-tutorial

Safe Reinforcement Learning via Shielding

https://github.com/GAMES-UChile/mogptk

SAMBA: Safe Model-Based & Active Reinforcement Learning

https://github.com/HaozheJasper/CBRL_KDD22

Reward Constrained Policy Optimization

https://github.com/LeoZhengZLY/vertex-net

Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks

https://github.com/MaximeBouton/AutomotiveSafeRL

Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments

https://github.com/SafeRL-Lab/Safe-Multi-Objective-MuJoCo

Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation

https://github.com/SimonRennotte/Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

https://github.com/abalakrishna123/recovery-rl

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

https://github.com/ammar-n-abbas/Predictive-Maintenance-BC-IOHMM-DRL

Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning

https://github.com/awesomericky/sim2real-grounding-simulator

Constrained Policy Optimization

https://github.com/dobro12/CPO

Constrained Policy Optimization

https://github.com/ethz-asl/rl-navigation

Constrained Policy Optimization

https://github.com/federico-pizarrobejarano/safe-control-gym

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

https://github.com/hari-sikchi/pytorch_CPO

Constrained Policy Optimization

https://github.com/huiyulhy/safe-control-gym

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

https://github.com/jachiam/cpo

Constrained Policy Optimization

https://github.com/jemaw/gym-safety

Constrained Policy Optimization

https://github.com/mahaitongdae/safety_index_synthesis

Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety

https://github.com/microsoft/intrepid

Provable Safe Reinforcement Learning with Binary Feedback

https://github.com/obastani/model-predictive-shielding

Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive Shielding

https://github.com/rcheng805/RL-CBF

End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

https://github.com/resuldagdanov/self-improving-RL

Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms

https://github.com/sapanachaudhary/pytorch-cpo

Constrained Policy Optimization

https://github.com/susumuota/gym-modeestimation

Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.