180: Reinforcement Learning

Intro topic: Grills News/Links: You can’t call yourself a senior until you’ve worked on a legacy project https://www.infobip.com/developers/blog/seniors-working-on-a-legacy-project Recraft might be the most powerful AI image platform I’ve ever used — here’s why https://www.tomsguide.com/ai/ai-image-video/recraft-might-be-the-most-powerful-ai-image-platform-ive-ever-used-heres-why NASA has a list of 10 rules for software development https://www.cs.otago.ac.nz/cosc345/resources/nasa-10-rules.htm AMD Radeon RX 9070 XT performance estimates leaked: 42% to 66% faster than Radeon RX 7900 GRE https://www.tomshardware.com/tech-industry/amd-estimates-of-radeon-rx-9070-xt-performance-leaked-42-percent-66-percent-faster-than-radeon-rx-7900-gre Book of the Show Patrick: The Player of Games (Ian M Banks) https://a.co/d/1ZpUhGl (non-affiliate) Jason: Basic Roleplaying Universal Game Engine https://amzn.to/3ES4p5i Patreon Plug https://www.patreon.com/programmingthrowdown?ty=h Tool of the Show Patrick: Pokemon Sword and Shield Jason: Features and Labels ( https://fal.ai ) Topic: Reinforcement Learning Three types of AI Supervised Learning Unsupervised Learning Reinforcement Learning Online vs Offline RL Optimization algorithms Value optimization SARSA Q-Learning Policy optimization Policy Gradients Actor-Critic Proximal Policy Optimization Value vs Policy Optimization Value optimization is more intuitive (Value loss) Policy optimization is less intuitive at first (policy gradients) Converting values to policies in deep learning is difficult Imitation Learning Supervised policy learning Often used to bootstrap reinforcement learning Policy Evaluation Propensity scoring versus model-based Challenges to training RL model Two optimization loops Collecting feedback vs updating the model Difficult optimization target Policy evaluation RLHF & GRPO ★ Support this podcast on Patreon ★