On-Policy Optimization of ANFIS Policies Using Proximal Policy Optimization
On the map: Deep reinforcement learning methods → RL theory and sample complexity
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 0 of its 6 references that are in the map. First posted on arXiv 22 Jun 2025.
Builds on
0 of 6 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Beyond the Boundaries of Proximal Policy Optimization
- 2024Linear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges
- 2024Transductive Off-policy Proximal Policy Optimization
- 2026ExO-PPO: an Extended Off-policy Proximal Policy Optimization Algorithm
- 2025Gradient-Based Neuroplastic Adaptation for Concurrent Optimization of Neuro-Fuzzy Networks
- 2026Sample-efficient Neuro-symbolic Proximal Policy Optimization
- 2016Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies
- 2025A Practical Introduction to Deep Reinforcement Learning