Visual Radial Basis Q-Network

TL;DR

This paper introduces a Radial Basis Function Network-based method for visual feature extraction in reinforcement learning, achieving comparable or better performance with fewer parameters compared to traditional deep learning approaches.

Contribution

The paper presents a novel RBFN-based approach for visual feature extraction in RL that requires fewer parameters and simplifies the process compared to convolutional or auto-encoder methods.

Findings

Achieves similar or better performance than deep CNNs and auto-encoders.

Uses fewer trainable parameters, simplifying the model.

Effective in Vizdoom environment for Q-learning tasks.

Abstract

While reinforcement learning (RL) from raw images has been largely investigated in the last decade, existing approaches still suffer from a number of constraints. The high input dimension is often handled using either expert knowledge to extract handcrafted features or environment encoding through convolutional networks. Both solutions require numerous parameters to be optimized. In contrast, we propose a generic method to extract sparse features from raw images with few trainable parameters. We achieved this using a Radial Basis Function Network (RBFN) directly on raw image. We evaluate the performance of the proposed approach for visual extraction in Q-learning tasks in the Vizdoom environment. Then, we compare our results with two Deep Q-Network, one trained directly on images and another one trained on feature extracted by a pretrained auto-encoder. We show that the proposed approach provides similar or, in some cases, even better performances with fewer trainable parameters while being conceptually simpler.