Here’s What We’ll Cover:
Reinforcement Learning
- Basics of reinforcement learning: agents, environments, rewards.
- Q-learning, deep Q-networks (DQN), policy gradients.
- Applications: game playing, robotics, autonomous vehicles.
Curriculum
- 8 Sections
- 55 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Reinforcement Learning8
- 1.1Reinforcement Learning Definition
- 1.2Main points in Reinforcement learning
- 1.3Difference between Reinforcement learning and Supervised learning
- 1.4Types of Reinforcement
- 1.5Elements of Reinforcement Learning
- 1.6Various Practical Applications of Reinforcement Learning
- 1.7Advantages and Disadvantages of Reinforcement Learning
- 1.8Implementation
- Reinforcement learning agents7
- Reinforcement Learning EnvironmentsReinforcement Learning Environments are a crucial component of Reinforcement Learning (RL), a branch of machine learning where an agent learns to make decisions by interacting with an environment. The agent’s goal is to maximize some notion of cumulative reward.7
- Reinforcement Learning Rewards6
- Q-learning9
- The DQN AgentThe DQN (Deep Q-Network) algorithm was developed by DeepMind in 2015. It was able to solve a wide range of Atari games (some to superhuman level) by combining reinforcement learning and deep neural networks at scale. The algorithm was developed by enhancing a classic RL algorithm called Q-Learning with deep neural networks and a technique called experience replay.7
- policy-gradient3
- Applications8
