JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
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Updated
Oct 15, 2024 - Python
JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
🚀 A fast safe reinforcement learning library in PyTorch
🤖 Elegant implementations of offline safe RL algorithms in PyTorch
PyTorch implementation of Constrained Reinforcement Learning for Soft Actor Critic Algorithm
🔥 Datasets and env wrappers for offline safe reinforcement learning
Blog Post about Curriculum Induction for Safe Reinforcement Learning
Poster about Curriculum Induction for Safe Reinforcement Learning
A Survey Analyzing Generalization in Deep Reinforcement Learning
A Multiplicative Value Function for Safe and Efficient Reinforcement Learning. IROS 2023.
Author implementation of DSUP(q) algorithms from the NeurIPS 2024 paper "Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning"
Correct-by-synthesis reinforcement learning with temporal logic constraints (CoRL)
Official Code Repository for the POLICEd-RL Paper: https://arxiv.org/abs/2403.13297
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