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* Title..: Pluralsight Understanding Algorithms for Reinforcement Learning
* Date...: 15-08-2018
* Type...: Bookware
* Disks..: 17x15mb

Release notes:

Traditional machine learning algorithms are used for
predictions and classification. Reinforcement learning is
about training agents to take decisions to maximize
cumulative rewards. In this course, Understanding Algorithms
for Reinforcement Learning, you'll learn basic principles of
reinforcement learning algorithms, RL taxonomy, and specific
policy search techniques such as Q-learning and SARSA.
First, you'll discover the objective of reinforcement
learning; to find an optimal policy which allows agents to
make the right decisions to maximize long-term rewards.
You'll study how to model the environment so that RL
algorithms are computationally tractable. Next, you'll
explore dynamic programming, an important technique used to
cache intermediate results which simplify the computation of
complex problems. You'll understand and implement policy
search techniques such as temporal difference learning (Q-
learning) and SARSA which help converge on to an optimal
policy for your RL algorithm. Finally, you'll build
reinforcement learning platforms which allow study,
prototyping, and development of policies, as well as work
with both Q-learning and SARSA techniques on OpenAI Gym. By
the end of this course, you should have a solid
understanding of reinforcement learning techniques, Q-
learning and SARSA and be able to implement basic RL
algorithms.

More info:
https://www.pluralsight.com

Install notes:

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