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Safe Exploration

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Safe Exploration is an approach to collect ground truth data by safely interacting with the environment.

Source: Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

코드

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

https://github.com/ISorokos/SafeML

공식 코드

SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

https://github.com/Ontroad/EPOPMN

공식 코드

Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints

https://github.com/Zhefan-Xu/DEP

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Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap

https://github.com/ahmadnagib/SARL-RRM

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Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks

https://github.com/akifumi-wachi-4/spolf

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Safe Policy Optimization with Local Generalized Linear Function Approximations

https://github.com/akjayant/mbppol

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Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm

https://github.com/archanabura/dope-doublyoptimisticpessimisticexploration

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DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning

https://github.com/arpanlosalka/m-safeucb

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Benefits of Monotonicity in Safe Exploration with Gaussian Processes

https://github.com/befelix/SafeMDP

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Safe Exploration in Finite Markov Decision Processes with Gaussian Processes

https://github.com/befelix/safe-exploration

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Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning

https://github.com/boschresearch/information-theoretic-safe-exploration

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Information-Theoretic Safe Exploration with Gaussian Processes

https://github.com/cccedric/conrft

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ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy

https://github.com/cube1324/d-atacom

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Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning

https://github.com/decisionforce/metadrive

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MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

https://github.com/deepmind/ai-safety-gridworlds

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AI Safety Gridworlds

https://github.com/ejcatt/aixijs_mentee

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Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal Agent

https://github.com/farazlotfi/underwater-object-tracking

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A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking

https://github.com/fujitsuresearch/safeexploration

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Safe Exploration Method for Reinforcement Learning under Existence of Disturbance

https://github.com/gavlegoat/safe-learning

공식 코드

Neurosymbolic Reinforcement Learning with Formally Verified Exploration

https://github.com/huawei-noah/hebo

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Effects of Safety State Augmentation on Safe Exploration

https://github.com/hychen-naza/SSA-RL

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Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic Environments

https://github.com/int-unl/atlas

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Atlas: Automate Online Service Configuration in Network Slicing

https://github.com/locuslab/ase

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Provably Safe PAC-MDP Exploration Using Analogies

https://github.com/lzqw/c-hac

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Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving

https://github.com/mahaitongdae/Feasible-Actor-Critic

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

https://github.com/manish-pra/safemac

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Near-Optimal Multi-Agent Learning for Safe Coverage Control

https://github.com/metadriverse/metadrive

공식 코드

MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

https://github.com/ml-jku/align-rudder

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Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

https://github.com/mlech26l/bayesian_nn_safety

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Infinite Time Horizon Safety of Bayesian Neural Networks

https://github.com/rjagerman/tois2019-safe-exploration-algorithm

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Safe Exploration for Optimizing Contextual Bandits

https://github.com/rl-boxes/safe-rl

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CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning

https://github.com/simonzhan-code/step-wise_saferl_pixel

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State-Wise Safe Reinforcement Learning With Pixel Observations

https://github.com/sjunges/shielding-POMDPs

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Enforcing Almost-Sure Reachability in POMDPs

https://github.com/team-daniel/advice

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Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding

https://github.com/xtji/cex-guided-rl

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Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)

https://gitlab.com/hdonancio/pumpscheduling

공식 코드

The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning

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