Happy new year everyone. A fantastic new year is here. Wishing everyone a great year ahead.
As I mentioned in the title, I am planning to study Reinforcement learning this year. For the last two years, I have concentrated on machine learning algorithms mainly related to Deep neural networks. Starting with Andrew Ng’s CS 229 in 2017, it has been a great journey so far.
2019 is the year of Reinforcement -learning
I have been following a lot of recent research papers and many of them are having a mixture of Deep learning models and Reinforcement techniques. This is a strong indication that 2019 in the year of reinforcement learning.
What is Reinforcement learning?
In reinforcement learning, we are training an agent in an environment. Each action of the agent gives a positive reward or negative reward. The goal of the agent is to maximize the reward.
Google beat the best Go player with AlphaGo using a combination of techniques that involves reinforcement learning(Deep Reinforcement Learning).
Uber recently solved Montezuma’s Revenge by Go-Explore
The biggest advantage is that we can combine the Deep learning networks and Reinforcement learning techniques together to create really powerful algorithms.
Plan of Study
I have gone through some basic understanding of RL last year in the following lectures:
[UC Berkeley] CS188 Artificial Intelligence by Pieter Abbeel
- Lecture 8: Markov Decision Processes 1
- Lecture 9: Markov Decision Processes 2
- Lecture 10: Reinforcement Learning 1
- Lecture 11: Reinforcement Learning 2
[Stanford] CS229 Machine Learning – Lecture 16: Reinforcement Learning by Andrew Ng
I watched these lectures long time back and since I was concentrating more on Deep learning , I did not follow up much on RL. So I am planning to start with the following Lecture series:
Deep learning Bootcamp :https://sites.google.com/view/deep-rl-bootcamp/labs
I am thinking of writing a blog for each of the lectures and labs associated with it.
That’s all for this blog. If you are also starting RL this new year let me know about it in the comments.
akshaynathr 