Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
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Updated
Apr 20, 2024 - Python
Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
OmniSafe is an infrastructural framework for accelerating SafeRL research.
NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
NeurIPS 2023: Safe Policy Optimization: A benchmark repository for safe reinforcement learning algorithms
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.
Multi-Agent Constrained Policy Optimisation (MACPO; MAPPO-L).
Reading list for adversarial perspective and robustness in deep reinforcement learning.
The Source code for paper "Optimal Energy System Scheduling Combining Mixed-Integer Programming and Deep Reinforcement Learning". Safe reinforcement learning, energy management
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.
Code for "Constrained Variational Policy Optimization for Safe Reinforcement Learning" (ICML 2022)
The Verifiably Safe Reinforcement Learning Framework
[ICLR 2024] The official implementation of "Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model"
Safe Multi-Agent Isaac Gym benchmark for safe multi-agent reinforcement learning research.
ICLR 2024: SafeDreamer: Safe Reinforcement Learning with World Models
LAMBDA is a model-based reinforcement learning agent that uses Bayesian world models for safe policy optimization
Implementations of SAILR, PDO, and CSC
Repository containing the code for the paper "Safe Model-Based Reinforcement Learning using Robust Control Barrier Functions". Specifically, an implementation of SAC + Robust Control Barrier Functions (RCBFs) for safe reinforcement learning in two custom environments
Implementation of PPO Lagrangian in PyTorch
Training (hopefully) safe agents in gridworlds
This repository has code for the paper "Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm" accepted at NeurIPS 2022.
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