PyTorch Reinforcement Learning Guide: From Supervised Learning to MDP, MRP, and Policy
This article focuses on the core framework of reinforcement learning: an agent takes actions in an environment, receives rewards, and aims to maximize long-term return. Reinforcement learning addresses sequential decision-making, delayed rewards, and the exploration-exploitation tradeoff—problems that static supervised learning cannot handle well. Keywords: Reinforcement Learning, Markov Decision Process, PyTorch. Technical Specification Details Primary Language … Read more