Discover Algorithms for Reward-Based Learning in R
Product describes and compares model-based and model-free learning algorithms in reinforcement learning, it will show the users what model based and model free and model based approaches can do for them.
Offered by
Difficulty Level
Intermediate
Completion Time
2h36m
Language
English
About Course
Who Is This Course For?
This course is intended for anyone who is interested in learning how RL model-based algorithms can generate goal-oriented policies and how to evaluate and optimize those policies. You should know how to program in R, but no prior experience in Reinforcement Learning is required. They will be shown R Example of Moving a Pawn with Changed Parameters, discount Factor, and Policy Improvement. They will be able to program a Model-Free Environment Using Monte Carlo and Q-Learning. In the end, they will be able to Build actions, rewards, and punishments through Simulated Annealing Approach using Visual Q-Learning Examples
Discover Algorithms for Reward-Based Learning in R
- About Course
- Who Is This Course For?
- Course Content
Course content
lessons • 2h36m total length
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