Engineering Notes
Thoughts and Ideas on AI by Muthukrishnan
Tag: Reinforcement Learning
Inverse Reinforcement Learning Inferring Goals From Behavior
Learn how inverse reinforcement learning lets AI agents discover hidden reward functions by observing expert behavior, and why it matters for agent alignment and autonomous systemsReward Shaping and Credit Assignment for Guiding Agent Learning
Master the art and science of designing reward functions and solving the credit assignment problem—the key to training agents that learn efficiently and align with human intentions.Curiosity-Driven Learning and Intrinsic Motivation in AI Agents
Discover how curiosity-driven learning enables AI agents to explore, learn, and adapt in sparse-reward environments through intrinsic motivation mechanisms.How AI Agents Learn from Themselves Through Self-Play and Iterative Refinement
Explore how AI agents improve through self-play and iterative refinement—from AlphaGo's game mastery to modern LLM self-correction techniques.Multi-Armed Bandits and the Exploration-Exploitation Dilemma in Agent Learning
Master the fundamental problem of sequential decision-making under uncertainty and learn how AI agents balance trying new actions versus exploiting known rewardsContextual Bandits for Balancing Exploration and Exploitation in Real-Time
Master contextual bandits—the algorithm behind personalized recommendations, A/B testing, and adaptive agents. Learn how to balance exploration and exploitation in real-time decision-making.Temporal Difference Learning and Q-Learning as the Engine of Agent Intelligence
Welcome to today's deep dive into one of the most influential algorithms in AI agent programming: **Temporal Difference...Policy Gradient Methods and Actor-Critic Architectures
Master the mathematics and implementation of policy gradient methods and actor-critic architectures—the foundation of modern agent learning systems.Bellman Equations and Value Functions for Optimal Decision-Making
## 1. Concept Introduction **In Simple Terms:** Imagine you're playing chess and trying to decide your next move. A be...An Introduction to Reinforcement Learning for AI Agents
## 1. Concept Introduction At its heart, Reinforcement Learning (RL) is about learning from trial and error. Imagine t...