Object Detection for Reinforcement Learning Agents

Introduction

• Traditional reinforcement learning uses images as input.
• Typically utilizes Convolutional Neural Networks (CNNs) for feature extraction.
• Aim: Maximize cumulative reward.

Proposed Approach

• Utilizes object detectors instead of CNNs for pre-processing.
• Converts images into bounding boxes and object locations.

Key Terms

• Object Detection: Identifying objects within an image and locating them via bounding boxes.
• Reinforcement Learning: Training agents to make sequences of decisions.
• Deep Q-Learning: A value-based reinforcement learning algorithm.

Relevant Research and References

• Generalizable Reinforcement Learning: When is it tractable? (D. Malik, et al.)
• Object Detection & Semantic Segmentation Techniques: Rich feature hierarchies, SSD, YOLO, Faster R-CNN.
• Frameworks & Tools: OpenAI Gym, PyTorch, Microsoft COCO, OpenCV.
• Additional Techniques: Learning non-maximum suppression, reward shaping.

Implications

• This approach could improve the efficiency of reinforcement learning by providing better input data through object detection.
• Potentially enhances the agent's understanding of the environment by focusing on crucial objects and their positions.