A new hands-on reinforcement learning course series is launching, starting with foundational concepts: how RL differs from supervised/unsupervised learning, the agent-environment loop, exploration-exploitation tradeoff, multi-armed bandits, and four action-selection strategies (greedy, ε-greedy, optimistic initialization, UCB). The motivation is clear — RL is now central to frontier LLM post-training pipelines (RLHF, GRPO, constitutional AI), making RL fluency increasingly essential for ML engineers. No prior RL background is required.
Sort: