- 1 How can I get better at reinforcement learning?
- 2 What is required for reinforcement learning?
- 3 What are the best resources to learn reinforcement learning?
- 4 Are there reward functions for reinforcement learning models?
- 5 Where does the car start in reinforcement learning?
- 6 When to use concept networks in reinforcement learning?
How can I get better at reinforcement learning?
Newbie’s Guide to Study Reinforcement Learning
- Stop the Deluge of Information.
- The Online Course.
- Have a Textbook Lying Around (and this will help you a lot!)
- Learn by coding, not just by reading.
- Playing around.
- Parameters are brittle but check for typos first!
- Go Broad.
What is required for reinforcement learning?
Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, without reference to an estimated probability distribution, shows poor performance. The case of (small) finite Markov decision processes is relatively well understood.
What are the best resources to learn reinforcement learning?
Deep Reinforcement Learning based techniques which are quite powerful and popular to solve many real life challenges using artificial intelligence. The course requirers understand of Machine Learning and Deep Learning basics as pre-requisites along with familiarity with Python and deep learning framework such as Keras/PyTorch.
Are there reward functions for reinforcement learning models?
Crafting reward functions for reinforcement learning models is not easy. It’s not easy for the same reason that crafting incentive plans for employees is not easy. We get things affectionately known as the cobra effect.
Where does the car start in reinforcement learning?
The car starts on a location on this line, the initial speed is one (i.e., one step horizontally or vertically), and it must cross the line from the other side. In the example code, the track and the start line location are encoded in a lossless image file. The start position is given separately as a line on the start line.
When to use concept networks in reinforcement learning?
As these problems get larger and more complex, which happens pretty easily with real-world systems, you want to start thinking about using techniques like concept networks instead of just constantly making more and more complex reward functions. Want to learn more about deep reinforcement learning, reward functions and concept networks?