Reinforcement Learning
Reinforcement Learning
4.71 (90 reviews)
9,120
students
8 hours
content
Jan 2023
last update
$69.99
regular price
What you will 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?
Related Topics
5048136
udemy ID
12/29/2022
course created date
1/28/2023
course indexed date
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