This course focuses on the fundamental concepts of Reinforcement Learning (RL) and its application to autonomous drones, structured into three main learning grains:
Grain 1: Key Concepts in Reinforcement Learning (RL)
An introduction to RL, explaining how agents learn optimal behaviors by interacting with their environment. Key ideas include states, actions, rewards, and policies that guide decision-making.
Grain 2: Exploration vs Exploitation
This grain covers the crucial dilemma faced by learning agents: balancing exploration (trying new actions to discover better rewards) with exploitation (choosing actions known to yield high rewards). This trade-off is essential for effective learning.
Grain 3: Q-Learning Algorithm
An overview of Q-Learning, a popular model-free RL algorithm. It learns the value of state-action pairs to derive optimal policies without requiring a model of the environment. This algorithm underpins many autonomous drone decision-making systems.
