8: Markov Decision Process (MDP)
The Markov Decision Process (MDP) is one of the most basic blocks of reinforcement learning architectures. A conceptual algorithm in its own right, it has been adapted into a great number of other algorithms, and recurs frequently in the current crop of AI/ML research.
MDP explores a data environment by using its evaluation of its current state (i.e. ‘where’ it is in the data) to decide which node of the data to explore next.
Besides its obvious applicability to chess and other strictly sequential games, MDP is also a natural contender for the procedural training of robotics systems.
151
209 reads
CURATED FROM
IDEAS CURATED BY
The Math Of Machine Learning
“
The idea is part of this collection:
Learn more about computerscience with this collection
The differences between Web 2.0 and Web 3.0
The future of the internet
Understanding the potential of Web 3.0
Related collections
Similar ideas to 8: Markov Decision Process (MDP)
3 Categories of Machine Learning
- Supervised learning: It has a set of labelled data to train the model on. In a way, it means supervising a machine by providing a ton of information about a particular case and giving it the case outcome.
- Unsupervised learning:
Read & Learn
20x Faster
without
deepstash
with
deepstash
with
deepstash
Personalized microlearning
—
100+ Learning Journeys
—
Access to 200,000+ ideas
—
Access to the mobile app
—
Unlimited idea saving
—
—
Unlimited history
—
—
Unlimited listening to ideas
—
—
Downloading & offline access
—
—
Supercharge your mind with one idea per day
Enter your email and spend 1 minute every day to learn something new.
I agree to receive email updates