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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.