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arXiv · 2507.01039

On-Policy Optimization of ANFIS Policies Using Proximal Policy Optimization

Kaaustaaub Shankar·Wilhelm Louw·Kelly Cohen
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Abstract

We present a reinforcement learning method for training neuro-fuzzy controllers using Proximal Policy Optimization (PPO). Unlike prior approaches that used Deep Q-Networks (DQN) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS), our PPO-based framework leverages a stable on-policy actor-critic setup. Evaluated on the CartPole-v1 environment across multiple seeds, PPO-trained fuzzy agents consistently achieved the maximum return of 500 with zero variance after 20000 updates, outperforming ANFIS-DQN baselines in both stability and convergence speed. This highlights PPO's potential for training explainable neuro-fuzzy agents in reinforcement learning tasks.

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Kaaustaaub Shankar, Wilhelm Louw, Kelly Cohen. 2025-06-22. On-Policy Optimization of ANFIS Policies Using Proximal Policy Optimization. https://arxiv.org/abs/2507.01039

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