Reinforcement Learning
Sub Category
- Other IT & Software
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Objectives
- Define what is Reinforcement Learning?
- Apply all what is learned using state-of-the art libraries like OpenAI Gym, StabeBaselines, Keras-RL and TensorFlow Agents
- Define what are the applications domains and success stories of RL?
- Define what are the difference between Reinforcement and Supervised Learning?
- Define the main components of an RL problem setup?
- Define what are the main ingredients of an RL agent and their taxonomy?
- Define what is Markov Reward Process (MRP) and Markov Decision Process (MDP)?
- Define the solution space of RL using MDP framework
- Solve the RL problems using planning with Dynamic Programming algorithms, like Policy Evaluation, Policy Iteration and Value Iteration
- Solve RL problems using model free algorithms like Monte-Carlo, TD learning, Q-learning and SARSA
- Differentiate On-policy and Off-policy algorithms
- Master Deep Reinforcement Learning algorithms like Deep Q-Networks (DQN), and apply them to Large Scale RL
- Master Policy Gradients algorithms and Actor-Critic (AC, A2C, A3C)
- Master advanced DRL algorithms like DDPG, TRPO and PPO
- Define what is model-based RL, and differentiate it from planning, and what are their main algorithms and applications?
Pre Requisites
- Machine Learning basics
- Deep Learning basics
- Probability
- Programming and Problem solving basics
- Python programming
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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