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Found 6 papers, 1 papers with code
Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
By introducing this benchmark, we aim to facilitate the evaluation and comparison of safety performance, thus fostering the development of reinforcement learning for safer, more reliable, and responsible real-world applications.
SafeDreamer: Safe Reinforcement Learning with World Models
Existing Safe Reinforcement Learning (SafeRL) methods, which rely on cost functions to enforce safety, often fail to achieve zero-cost performance in complex scenarios, especially vision-only tasks.
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research
AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns.
Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation
The dependence of ML models with dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections.
A new method for life table and life expectancy calculation
The theory and method of life table described in this paper are simple and easy to understand.
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Large-scale distributed optimization is of great importance in various applications.
