What you’ll learn
- 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?
How to Enroll Reinforcement Learning course?
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