2025/06/22 by Shankar, Kaaustaaub, Wilhelm Louw, Louw, Wilhelm +2
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Cloud Computing and Resource Management #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2507.01039
openalex publication_date 2025/06/22 · openalex created_date 2025/10/20 · openalex updated_date 2026/07/28
We propose a reinforcement learning (RL) approach for training neuro-fuzzy controllers using Proximal Policy Optimization (PPO). Building on prior work that applied Deep Q-Learning to Adaptive Neuro-Fuzzy Inference Systems (ANFIS), our method replaces the off-policy value-based framework with a stable on-policy actor-critic loop. We evaluate this approach in the CartPole-v1 environment using multiple random seeds and compare its learning performance against ANFIS-Deep Q-Network (DQN) baselines. It was found that PPO-trained fuzzy agents achieved a mean return of 500 +/- 0 on CartPole-v1 after 20000 updates, showcasing less variance than prior DQN-based methods during training and overall faster convergence. These findings suggest that PPO offers a promising pathway for training explainable neuro-fuzzy controllers in reinforcement learning tasks.