Two fundamental concepts of reinforcement learning

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Reinforcement learning involves learning from different actions and using a reward function to rate their outcomes. The agent explores the world by taking various actions, which are then rated by the reward function. The algorithm identifies the actions that tend to be better and continues the learning loop. There are technicalities in making it work efficiently and with other systems like GPT. In chess, the behavior choice is moving the pieces according to the rules, with the goal of not having pieces taken and ultimately winning the game.

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Today’s show:

Covariant CEO Peter Chen joins Jason to discuss the future of AI in robotics (1:33), the key concepts of reinforcement learning (13:50), and much more!

Time stamps:

(0:00) Covariant CEO Peter Chen joins Jason

(1:33) AI’s role in robotics and its value in e-commerce warehouses

(7:38) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist

(8:59) Reinforcement learning today and the AlphaGo moment

(13:50) The 2 key concepts of reinforcement learning

(17:35) Approaches to accessing data for AI in robotics

(20:35) Robotics hardware

(22:50) Vanta - Get $1000 off your SOC 2 at https://vanta.com/twist

(23:57) Covariant's hardware and software in use

(27:32) The importance of adaptability

(32:40) LinkedIn Marketing - Get a $100 LinkedIn ad credit at https://linkedin.com/nextunicorn