Description:
Eyes RL introduces a novel reinforcement learning approach to image recognition inspired by human vision. By mimicking the way our eyes selectively focus on areas of interest, this project demonstrates accurate digit recognition using a limited 4x4 input window, challenging the traditional reliance on large inputs and convolutional neural networks (CNNs).
Key Features:
- Focus-Based Learning: Simulates human-like vision by using a small, moving window to analyze images.
- Reinforcement Learning Agent: An LSTM-based agent learns optimal movement and zoom actions to maximize recognition accuracy.
- Efficient Architecture: Achieves competitive performance without the use of CNNs.
- MNIST Benchmark: Demonstrates the effectiveness of Eyes RL on the MNIST dataset, achieving a 69% accuracy with only a 4x4 input.