Exploitation vs Exploration: The Learning Dilemma in AI

We unpack the exploration-exploitation dilemma in machine learning and AI, from the classic multi-armed bandit to sophisticated reinforcement learning. Learn how algorithms balance sticking with known rewards and trying new options, explore strategies like epsilon-greedy, Thompson sampling, and UCB, and explore intrinsic motivation, count-based and prediction-based rewards, as well as cutting-edge ideas like ICM and RND. We'll also discuss why adding purposeful randomness can boost discovery when rewards are sparse or noisy.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC