Cooperative wake steering for wind farm power enhancement under dynamic inflow conditions: An entropy-regularized multi-agent reinforcement learning approach

AI-Enabled Performance-Based Procurement and Life-Cycle Maintenance of Highway Bridges: Integrating Single-Bid Risk Analytics and PPP Payment Optimization

Model-based life-cycle evaluation indicates that AI-optimized PPP contracts reduce bridges reaching emergency condition by 30%–40% over a 30-year horizon while lowering life-cycle costs by 8%–12% compared with rule-based policies, providing infrastructure agencies and private concessionaires with an integrated AI-driven life-cycle management platform.

A hybrid physics-aware multi-objective analog integrated circuit design framework with deep reinforcement learning

Extension-layer fine-tuning for transfer learning in multi-agent reinforcement learning-based HVAC control

LLM powered adaptive traffic signal control via knowledge-enhanced prompt and reinforcement learning fine-tuning

Dynamic obstacle avoidance for autonomous vehicles in complex traffic environments: A guidance-enhanced deep reinforcement learning approach

Residual RL on a PSO-tuned Fuzzy Controller for Mobile Robot Trajectory Tracking

This paper presents a two-wheeled mobile robot trajectory-tracking controller combining a particle swarm optimization (PSO)-tuned fuzzy logic controller (FLC) with a residual reinforcement learning (RL) correction layer.PSO tuning reduces the global distance error by 35% and the integral absolute error by 44% over the initial FLC.The residual RL layer further reduces the global distance error by approximately 2.3% and improves cornering-region tracking by 3.9% in RMSE, 4.7% in IAE, and 5.2% in peak distance error.The proposed controller also reduces the global distance error by 41% and 66% relative to independently tuned PID and fuzzy-PID baselines.Trained across four trajectory families with a held-out test split, the generalized agent reduces the average test distance error by 18% relative to the tuned FLC baseline.These results show that a lightweight residual correction improves both accuracy and generalization while preserving the fuzzy controller's interpretability.