reinforcement learning

Autonomous Healthcare Control System (AHCS)

Today's healthcare systems represent complex adaptive systems that involve stochastic demand, tight coupling between subsystems, and nonlinearity of resource constraints. This paper proposes the Autonomous Healthcare Control System (AHCS), which brings together such approaches as stochastic state space models, Markov D...

Deep reinforcement learning-based Dynamic routing for ultra-reliable low-latency communication in 6G networks

Ultra-reliable low-latency communication (URLLC) is one of the hardest service classes that 6G has to carry, because a packet that misses its deadline is useless even if it eventually arrives. Routing based on fixed shortest paths ignores queue build-up, and link-state protocols that rebuild their tables every few mill...

Adaptive Self–Mutual Error Correction in World Models: A Unified Framework for State-Dependent Switching Between Internal Repair and Cross-Model Correction

World models provide internal representations of environmental dynamics and support prediction, planning, simulation, and decision-making. However, errors in world models may persist when a system relies exclusively on its own internal representations, while external correction can introduce communication costs, confli...

Scalable Multi-UAV Target Assignment via Permutation-Invariant Reinforcement Learning and Conditional Control-Barrier-Function Safety Filtering

Coordinating multiple unmanned aerial vehicles (UAVs) to assign targets and generate collision-free trajectories in dynamic, obstacle-dense environments is a challenging problem with applications in search-and-rescue, surveillance, and precision agriculture. Existing deep reinforcement learning approaches for joint tar...

AuditTune campaign evidence: experiment ledger, agent-written planner code, and figure-generation scripts for auditable policy adjustment in reinforcement learning control

Evidence package for the manuscript "An Auditable Learning-Curve-Driven Automatic Policy Adjustment Framework for Engineering Reinforcement Learning Control Systems" (EAAI submission). The dataset contains the complete experiment ledger, the planner source code written by the diagnosis-driven agent, and the scripts tha...

Robust Autonomous Lunar Landing Under Uncertain Physics

The project studies whether training an agent across randomized environment physics can improve its performance on previously unseen conditions compared with training under fixed physics. It uses the LunarLander-v3 environment and Proximal Policy Optimization (PPO), alongside a custom PyTorch actor–critic implementatio...

A Mobile-Centric Approach to Improving the Performance of Decentralized Wireless Networks

The proliferation of wireless devices and intelligent environments has led to increasingly dense, interference-prone Wireless Local Area Network (WLAN) deployments. Recent amendments, such as IEEE 802.11ax/be, aim to enhance spatial reuse (SR) by optimizing spectrum efficiency. However, current SR initiatives face sign...

Controlled rough SDEs, pathwise stochastic control and dynamic programming principles

We study stochastic optimal control of rough stochastic differential equations (RSDEs). This is in the spirit of the pathwise control problem (Lions–Souganidis 1998, Buckdahn–Ma 2007; also Davis–Burstein 1992), with renewed interest and recent works drawing motivation from filtering, SPDEs, and reinforcement learning....

Agentic AIGC: A Survey of Visual Generation Agents

Visual generation is becoming a general component of AI infrastructure, supporting content creation across design, entertainment, education, science, and industry. Generative and multimodal models have substantially improved single-pass synthesis. At the same time, multi-step visual creation requires spatial precision,...

AETERNA Quantum Matrix Acceleration & Zero-Copy Sovereign IPC: Sub-Microsecond Lock-Free Shared Memory Ring Buffers, Aharonov-Bohm Topological Phase Protection on NVIDIA Ada Lovelace sm_89, and Zen 4 AVX-512 Concurrency

AETERNA QUANTUM MATRIX ACCELERATION & ZERO-COPY SOVEREIGN IPC: Sub-Microsecond Lock-Free Shared Memory Ring Buffers, Aharonov-Bohm Topological Phase Protection on NVIDIA Ada Lovelace sm_89, and Zen 4 AVX-512 Concurrency Author: Dimitar Stavrev Prodromov (AETERNA Technologies HQ, Pomorie, Bulgaria)Authority Signature: 0...

PIPO: Physics-informed deep reinforcement learning for pareto-optimal control for active suspension system

Stable, high-temperature acoustic sonoluminescence using learning-based control

Abstract Achieving high collapse temperatures under stable operating conditions is a central challenge in acoustic cavitation and sonoluminescence, as instabilities typically limit energy focusing. Conventional high-amplitude, low-frequency driving favors high collapse temperatures but simultaneously triggers shape ins...