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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.