Deep Reinforcement Learning
Deep reinforcement learning combines deep neural networks with reinforcement learning, enabling agents to learn decision-making in high-dimensional complex environments.
Contents:
- Deep RL Fundamentals — Deep RL overview, key challenges, algorithm taxonomy
- DQN — Experience replay, target networks, Double DQN
- PPO — Proximal policy optimization, clipped objective, GAE
- SAC — Maximum entropy RL, soft value functions, automatic temperature tuning