Visual Radial Basis Q-Network

• Published in: Pattern Recognition and Artificial Intelligence Vol. 13364; pp. 318 - 329
• Main Authors: Hautot, Julien, Teuliere, Céline, Azzaoui, Nourddine
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Computer vision; Radial basis function network; Reinforcement learning; State representation
• ISBN: 3031092813; 9783031092817
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-09282-4_27

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.