Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
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Updated
Jun 13, 2024 - Python
Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
The repository is for safe reinforcement learning baselines.
NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
NeurIPS 2023: Safe Policy Optimization: A benchmark repository for safe reinforcement learning algorithms
Multi-Agent Constrained Policy Optimisation (MACPO; MAPPO-L).
Open-source reinforcement learning environment for autonomous racing — featured as a conference paper at ICCV 2021 and as the official challenge tracks at both SL4AD@ICML2022 and AI4AD@IJCAI2022. These are the L2R core libraries.
The Source code for paper "Optimal Energy System Scheduling Combining Mixed-Integer Programming and Deep Reinforcement Learning". Safe reinforcement learning, energy management
Reading list for adversarial perspective and robustness in deep reinforcement learning.
[ICLR 2024] The official implementation of "Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model"
Code for "Constrained Variational Policy Optimization for Safe Reinforcement Learning" (ICML 2022)
Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.
ICLR 2024: SafeDreamer: Safe Reinforcement Learning with World Models
Safe Multi-Agent Isaac Gym benchmark for safe multi-agent reinforcement learning research.
[IEEE TAI] Safe Multi-Agent Reinforcement Learning to Make decisions in Autonomous Driving.
[ICLR 2025] Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning.
The Verifiably Safe Reinforcement Learning Framework
Implementation of PPO Lagrangian in PyTorch
Repository containing the code for safe reinforcement learning in two custom environments
[T-ITS'24] A safety-aware human-in-the-loop Reinforcment Learning (SafeHiL-RL) approach for end-to-end autonomous driving.
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