🔑Reinforcement learning unlocks the ability for programs to perform complex tasks by conducting experiments in an environment.
💡Implementing reinforcement learning from scratch requires less theoretical knowledge and allows for hands-on learning.
🧠Neural networks in reinforcement learning evolve their architecture and weights as training progresses.
⚙️Directed acyclic graphs (DAGs) are used to represent neural networks in reinforcement learning.
🕒Training in reinforcement learning involves iterations of evaluation, selection, and mutation to improve network performance.