Reinforcement Learning
type of machine learning where an agent learns how to behave in an environment by performing actions and receiving rewards or penalties in return, aiming to maximize the cumulative reward over time
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Videos Mentioning Reinforcement Learning
Pieter Abbeel: Deep Reinforcement Learning | Lex Fridman Podcast #10
Lex Fridman
The core machine learning paradigm discussed, focusing on how systems learn through trial and error and sparse rewards, its challenges, and potential future directions.
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15
Lex Fridman
A machine learning paradigm where agents learn through trial and error by receiving rewards or penalties for their actions. Kaelbling mentions 'reinventing' it at SRI and humorously referring to rewards as 'pleasures'.
Why OpenAI's o1 Is A Huge Deal | YC Decoded
Y Combinator
A machine learning technique used to train o1 by allowing it to learn through trial and error with rewards and punishments, including generating synthetic chains of thought.
How Intelligent Is AI, Really?
Y Combinator
Environments for RL are discussed as a common approach in AI development, but the speaker cautions against them as a sole measure of progress, likening it to 'whack-a-mole' and emphasizing the need for generalization without predefined environments.
AI Whistleblower: We Are Being Gaslit By The AI Companies! They’re Hiding The Truth About AI!
The Diary Of A CEO
A type of machine learning where models are trained iteratively on examples to acquire capabilities, with data annotation being a key part of the process.
Stanford CS230 | Autumn 2025 | Lecture 8: Agents, Prompts, and RAG
Stanford Online
A type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward, which has a specific definition in RL separate from agentic workflows.