Safe Reinforcement Learning
76 papers with code • 0 benchmarks • 1 datasets
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Use these libraries to find Safe Reinforcement Learning models and implementationsMost 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
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
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.
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
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.
Reward Constrained Policy Optimization
Solving tasks in Reinforcement Learning is no easy feat.