This study proposes an explainable, data-driven framework integrating active-reward proximal policy optimization (AR-PPO), which successfully distills black-box AI strategies into verifiable, physics-informed standard operating procedures (SOPs), providing a highly transparent and robust solution for autonomous windshear escape and future competency-based flight training.
Abstract
Low-altitude micro downbursts pose a severe threat to aviation safety, yet conventional control approaches and standard deep reinforcement learning (DRL) often fail due to explicit modeling difficulties and sparse reward constraints. To address these challenges, this study proposes an explainable, data-driven framework integrating active-reward proximal policy optimization (AR-PPO). A bilevel optimization architecture driven by meta-gradients is developed to dynamically discover optimal reward functions without human intervention. Furthermore, a policy distillation pipeline utilizing wavelet-multivariate singular spectrum analysis (W-MSSA) and classification and regression trees (CART) is proposed to translate high-frequency continuous neural outputs into discrete, pilot-readable rules. Simulation results on a B737-800 model demonstrate that AR-PPO effectively overcomes the “stall trap” by autonomously learning to trade altitude for airspeed, outperforming static-reward baselines and empirical human pilots in extreme, zero-shot windshear encounters (22.0 m/s downdraft). Ultimately, the proposed framework successfully distills black-box AI strategies into verifiable, physics-informed standard operating procedures (SOPs), providing a highly transparent and robust solution for autonomous windshear escape and future competency-based flight training.
Comparative control theory analysis reveals a fundamental trade-off: transitioning from continuous to discrete rule-based control induces high-frequency Bang-Bang actuation and a stable bimodal limit cycle.
An algorithm-agnostic Distributionally Robust MARL framework integrating an adaptive Contextual-Bandit Worst-Case Estimator (CB-WCE) co-evolves with the traffic controllers by dynamically generating adversarial demand mixtures during training, demonstrating the framework's scalability and potential for resilient real-world urban deployment.
Shuwei Pei, Joran Borger, Arda Kosay et al.· 0 citations
The problems of partial observability and sensor shortage pose a significant challenge for autonomous Unmanned Aerial Vehicles (UAVs) as they prove to be challenging for conventional Deep Reinforcement Learning (DRL) methods to undertake well under such conditions. In this paper, a memory-augmented Proximal Policy Optimization (PPO) model extended using a Long Short-Term Memory (LSTM) network is proposed as a solution to such challenges. The observation space is constructed from 2D LiDAR and Inertial Measurement Unit (IMU) data to sense simultaneously external observation and internal state of motion, whereas the action space consists of continuous velocity commands. A shaped reward function is optimized for encouraging safe target approaching, obstacle avoidance, and convergence speed. Experimental outcomes show that the PPO-LSTM described herein achieves smoother paths, more robust reward convergence, and a much lower rate of collision than regular PPO. It also generalizes to new environments with movable obstacles. Qualitatively, the success rate increased from 64.5% to 83.9%, collision frequency reduced by over 70%, and path efficiency increased from 0.60 to 0.85, without suffering from unstable training behavior
M. Haddad, Dhayaa Khudher· Kufa journal of Engineering· 0 citations
This work proposes Single-rollout Autoregressive Policy Optimization (SAPO), a low-memory and compute-efficient framework in which the policy and value functions share a single autoregressive backbone, and introduces a trajectory-level generalized advantage estimator that combines lambda-returns with batch normalization.
Deploying reinforcement learning (RL) agents for heating, ventilation, and air-conditioning (HVAC) control across climatically diverse buildings is challenging, as policies trained in one climate often fail to generalize to others. We investigate transfer and meta-reinforcement learning for building climate control using Sinergym with a custom EnergyPlus model of a real room, comparing Double Deep Q-Network (DDQN) and Model-Agnostic Meta Learning DDQN (MAML-DDQN) across three transfer scenarios on a target very hot climate. Fine-tuned DDQN reduces temperature violations by a factor of 35 over the from-scratch baseline and improves reward by 86% over zero-shot transfer, while MAML-DDQN converges in less than half the fine-tuning episodes and exhibits substantially lower variance across seeds. Results highlight a key trade-off: MAML-DDQN is preferable at adaptation-focused deployments, while fine-tuned DDQN is superior when maximum performance is the objective.
Ulaş Filiz, Sajjad Baghaee, Mubeen Ahmed Fayyaz et al.· Signal Processing and Commun...· 0 citations
This work proposes AgentOPSD, a critic-free, recursive method for turn-level credit assignment in agentic reinforcement learning that aggregates token-level teacher-student log-probability gaps into turn-level evidence and recursively updates a Bayesian belief state in log-odds space.
Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
