Deep Reinforcement Learning in Action

Chapter 10. Interpretable reinforcement learning: Attention and relational models

This chapter covers

• Implementing a relational reinforcement algorithm using the popular self-attention model
• Visualizing attention maps to better interpret the reasoning of an RL agent
• Reasoning about model invariance and equivariance
• Incorporating double Q-learning to improve the stability of training

Hopefully by this point you have come to appreciate just how powerful the combination of deep learning and reinforcement learning is for solving tasks previously thought to be the exclusive domain of humans. Deep learning is a class of powerful learning algorithms that can comprehend and reason through complex patterns and data, and reinforcement learning is the ...