Safe Reinforcement Learning
77 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Safe Reinforcement Learning
Libraries
Use these libraries to find Safe Reinforcement Learning models and implementations
safe-control-gym
Most implemented papers
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
We introduce a general approach for seeking a stationary point in high dimensional non-linear stochastic optimization problems in which maintaining safety during learning is crucial.
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
data-and-decision-lab/self-improving-RL
•
•
In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods.
NLBAC: A Neural Ordinary Differential Equations-based Framework for Stable and Safe Reinforcement Learning
liqunzhao/neural-ordinary-differential-equations-based-lyapunov-barrier-actor-critic-nlbac
•
•
Reinforcement learning (RL) excels in applications such as video games and robotics, but ensuring safety and stability remains challenging when using RL to control real-world systems where using model-free algorithms suffering from low sample efficiency might be prohibitive.
Off-Policy Primal-Dual Safe Reinforcement Learning
Results on benchmark tasks show that our method not only achieves an asymptotic performance comparable to state-of-the-art on-policy methods while using much fewer samples, but also significantly reduces constraint violation during training.
Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation
Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications.
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
SimonRennotte/Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
•
•
Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks.
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human's intervention decisions.
Safe Reinforcement Learning via Shielding
DanielLSM/safe-rl-tutorial
•
•
In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions.
Logically-Constrained Reinforcement Learning
With this reward function, the policy synthesis procedure is "constrained" by the given specification.
A Lyapunov-based Approach to Safe Reinforcement Learning
In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints.
