Reinforcement Learning (RL)

Fundamentals

Agents learn by receiving rewards in an environment.

Reinforcement Learning optimizes an agent’s policy by interacting with an environment and receiving rewards.

Core elements: States, actions, rewards, transition dynamics.
Methods: Value-based (Q-learning), policy gradient, actor-critic approaches.
Challenges: Exploration vs. exploitation, stability, transfer from simulation.
Practice: Reward design, safety constraints, off-policy evaluation.