Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2024

N. SanghiDeep Reinforcement Learning with Pythonhttps://doi.org/10.1007/979-8-8688-0273-7_5

5. Function Approximation and Deep Learning

Nimish Sanghi¹  

(1)

Bangalore, India

 

The previous three chapters looked at various approaches to planning and control—first at the dynamic programming (DP), then at the Monte Carlo approach (MC), and finally at the temporal difference (TD) approach. In all these approaches, you saw problems where the state space and actions were discrete. Only in the previous chapter, toward the end, did I talk about Q-learning in a continuous state space. You discretized the state values using an arbitrary approach and trained a learning ...