Thread by @Sauain

𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 🤖

-A type of Machine Learning (What is it?)
-It's working
-Real-life Applications
-A book for learning RL
-Limitations

Let's learn about all these things in this thread!!

🧵👇

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴?

- A machine learning area.

-In RL an ML model is trained to take a sequence of decisions.

-If the model takes a correct decision it will get awarded otherwise negative points will be given.

Perfect Explanation👇

𝗪𝗼𝗿𝗸𝗶𝗻𝗴

Environment: Physical world in which the agent operates

State: Current situation of the agent

Reward: Feedback from the env.

Policy: Method to map agent’s state to actions

Value: Future reward that an agent would receive by taking an action in particular state

Let’s take the game of PacMan where the goal of the agent (PacMan) is to eat the food in the grid while avoiding the ghosts on its way.

The grid world is the interactive environment for the agent.

PacMan receives a reward for eating food and punishment if it gets killed by the ghost (loses the game).

The states are the location of PacMan in the grid world and the total cumulative reward is PacMan winning the game.

𝗥𝗲𝗮𝗹-𝗹𝗶𝗳𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀

-Building AI for playing games.
-Robot Navigation
-Industrial Automation
-Dialog Agents(Text, Speech)
-Online Stock Trading

𝗕𝗼𝗼𝗸 𝗳𝗼𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗟

A good book to learn about Reinforcement Learning is "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto

You can download/buy the book from this link! 👇

🔗 incompleteideas.net/book/the-book-…

𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀

-Training process can be time-consuming

-Can do many mistakes while learning

-Too much reinforcement learning can lead to an
overload of states, which can diminish the results.

-Reinforcement learning needs a lot of data and a lot of computation.

Hey All,

Hope I am able to make things easy to understand for you :)

If you like my content please retweet the first tweet of the thread!!

Sorry for not putting a thread yesterday, was busy with some personal stuff 😅