Uncertainty-aware deep reinforcement learning optimised construction fleet management: A digital twin approach

Uncertainty-aware reinforcement learning for joint optimization of data center operations and electricity market bidding

A multi-agent deep reinforcement learning framework for coordinated urban traffic signal and parking lot exit control

Deep reinforcement learning for adaptive rack repositioning and pod sequencing in robotic mobile fulfillment systems via multi-fidelity simulation optimization

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.