#Reward Design

Articles about Reward Design — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with reward design. ← All posts

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Pratik Dhanave · ·6 min read

Markov Decision Processes — The Math That Frames Every RL Problem

Behind every reinforcement learning algorithm is one formal structure: the Markov decision process. It's the precise way of writing down "an agent making a sequence of decisions under uncertainty," and once a problem is framed as an MDP, the whole toolkit of RL applies to it. This post builds the MDP from its parts and explains the one assumption — the Markov property — that makes the entire theory work.

Behind every RL algorithm is one structure: the Markov decision process (states, actions, transitions, reward, discount). The Markov property that makes it tractable, why state design matters, and reward design as RL's subtle, decisive, error-prone part (reward hacking) — the problem RLHF later solves.

#Reinforcement Learning#Markov Decision Process#Reward Design#Fundamentals#Machine Learning#AI Engineering

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.