MDPs: Markov Decision Processes Decision Making Under Uncertainty using POMDPsjl
This course introduces Markov Decision Processes (MDPs) and Decision Making Under Uncertainty using POMDPs.jl. It covers topics such as MDP definition, Grid World environment, state and action spaces, transition and reward functions, discount factor, QuickPOMDPs, MDP and RL solvers, and a Pluto notebook. Students will gain an understanding of how to use MDPs to make decisions under uncertainty. ▼
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Course Feature
Cost:
Free
Provider:
Youtube
Certificate:
Paid Certification
Language:
English
Start Date:
On-Demand
Course Overview
❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [February 21st, 2023]
Intro.
MDP definition.
Grid World.
State space.
Action space.
Transition function.
Reward function.
Discount factor.
QuickPOMDPs.
MDP solvers.
RL solvers.
Pluto notebook.
Grid World environment.
Grid World actions.
Grid World transitions.
Grid World rewards.
Grid World discount.
Grid World termination.
Grid World MDP.
Solutions (offline).
Value iteration.
Transition probability distribution.
Using the policy.
Visualizations.
Reinforcement learning.
TD learning.
Q-learning.
SARSA.
Solutions (online).
MCTS.
MCTS visualization.
Simulations.
Extras.
References.
(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
This course provides an introduction to Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs), which are powerful tools for decision making under uncertainty. It covers the fundamentals of MDPs, including definitions, grid world environments, state and action spaces, transition and reward functions, and discount factors. It also covers MDP solvers, RL solvers, and Pluto notebooks.
Possible Development Paths include data science, machine learning, artificial intelligence, robotics, and computer science.
Learning Suggestions for learners include studying related topics such as probability theory, linear algebra, calculus, and optimization. Additionally, learners should practice coding and implementing algorithms in Python or other programming languages. They should also explore different reinforcement learning algorithms and apply them to different problems.
Course Provider
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