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Proximal policy optimization for formation navigation and obstacle avoidance
In this paper, a formation control problem of second-order holonomic agents is considered, where agents navigate around obstacles using proximal...
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A policy primer and roadmap on AI worker surveillance and productivity scoring tools
Algorithmic worker surveillance and productivity scoring tools powered by artificial intelligence (AI) are becoming prevalent and ubiquitous...
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Parameter-Free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy Gradients
Approximation of the value functions in value-based deep reinforcement learning induces overestimation bias, resulting in suboptimal policies. We...
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“Learning as a Strategy” for Better EU Policy Understanding and Implementation in the Digital Era
The European Commission established the Better Regulation agenda of the European Union (EU) in 2015, but problems in EU implementation persist. A...
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Collaborative framework for UAVs-assisted mobile edge computing: a proximity policy optimization approach
Recently, unmanned aerial vehicles (UAVs) have been widely used in mobile edge computing (MEC) scenarios due to their flexibility, rapid deployment,...
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FeMIP: detector-free feature matching for multimodal images with policy gradient
Feature matching for multimodal images is an important task in image processing. However, most methods perform image feature detection, description,...
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Hybrid cryptosystem based healthcare data sharing with access control policy in cloud environment
Healthcare cloud computing environments are expanding quickly, and security and confidentiality of patient records are top priorities. Academics and...
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Optimal policy trees
We propose an approach for learning optimal tree-based prescription policies directly from data, combining methods for counterfactual estimation from...
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APPCorp: a corpus for Android privacy policy document structure analysis
With the increasing popularity of mobile devices and the wide adoption of mobile Apps, an increasing concern of privacy issues is raised. Privacy...
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Efficient policy evaluation by matrix sketching
In the reinforcement learning, policy evaluation aims to predict long-term values of a state under a certain policy. Since high-dimensional...
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Net versus relative impacts in public policy automation: a conjoint analysis of attitudes of Black Americans
The use of algorithms and automated systems, especially those leveraging artificial intelligence (AI), has been exploding in the public sector, but...
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Policy Representation Opponent Sha** via Contrastive Learning
To acquire results with higher social welfare in social dilemmas, agents need to maintain cooperation. Independent agents manage to navigate social...
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Energy-Based Policy Constraint for Offline Reinforcement Learning
Offline RL suffers from the distribution shift problem. One way to address this issue is to constrain the divergence between the target policy and...
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Basic flight maneuver generation of fixed-wing plane based on proximal policy optimization
Autonomous agile flight control has been a challenging problem due to complex highly nonlinear dynamics, and generating feasible basic flight...
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Resilience & Vulnerability: Concepts and Policy Contexts
Climate change is an unparalleled global challenge, with profound implications for the environment, societies, and economies. As the Earth’s climate...
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Authorization and Policy Enforcement
The previous chapters covered the mechanics of authorizing an API call and authenticating a user. This chapter will discuss authorization vs. the...
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Automated cloud resources provisioning with the use of the proximal policy optimization
Many modern applications, both scientific and commercial, are deployed to cloud environments and often employ multiple types of resources. That...
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A distributed and energy-efficient KNN for EEG classification with dynamic money-saving policy in heterogeneous clusters
Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in...
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UAVs rounding up inspired by communication multi-agent depth deterministic policy gradient
UAVs rounding up is a game between UAV swarm and targets. The main challenge lies in achieving efficient collaboration between UAVs and the setting...
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Resource Allocation Using Deep Deterministic Policy Gradient-Based Federated Learning for Multi-Access Edge Computing
The study focuses on utilizing the computational resources present in vehicles to enhance the performance of multi-access edge computing (MEC)...