Search Results for author:
Found 9 papers, 1 papers with code
Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees
Identifying safe areas is a key point to guarantee trust for systems that are based on Deep Neural Networks (DNNs).
Improving Deep Policy Gradients with Value Function Search
Deep Policy Gradient (PG) algorithms employ value networks to drive the learning of parameterized policies and reduce the variance of the gradient estimates.
Safe Deep Reinforcement Learning by Verifying Task-Level Properties
In this paper, we investigate an alternative approach that uses domain knowledge to quantify the risk in the proximity of such states by defining a violation metric.
Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation
Our evaluation compares the benefits of computing the number of violations using standard hard-coded properties and the ones generated with CROP.
Curriculum Learning for Safe Mapless Navigation
To this end, we present a CL approach that leverages Transfer of Learning (ToL) and fine-tuning in a Unity-based simulation with the Robotnik Kairos as a robotic agent.
Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation
We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm.
Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation
Aquatic navigation is an extremely challenging task due to the non-stationary environment and the uncertainties of the robotic platform, hence it is crucial to consider the safety aspect of the problem, by analyzing the behavior of the trained network to avoid dangerous situations (e. g., collisions).
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning
The combination of Evolutionary Strategies (ES) and Deep Reinforcement Learning (DRL) has been recently proposed to merge the benefits of both solutions.
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification
In this paper, we present a semi-formal verification approach for decision-making tasks, based on interval analysis, that addresses the computational demanding of previous verification frameworks and design metrics to measure the safety of the models.
