๐ฅ๐ฒ๐ถ๐ป๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐บ๐ฒ๐ป๐ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ค
-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!!
๐งต๐
-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!!
๐งต๐
๐ช๐ต๐ฎ๐ ๐ถ๐ ๐ฅ๐ฒ๐ถ๐ป๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐บ๐ฒ๐ป๐ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด?
- 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 Explanation๐
- 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 Explanation๐
๐ช๐ผ๐ฟ๐ธ๐ถ๐ป๐ด
Environment: Physical world in which the agent operates
State: Current situation of the agent
Reward: Feedback from the env.
Policy: Method to map agentโs state to actions
Value: Future reward that an agent would receive by taking an action in particular state
Environment: Physical world in which the agent operates
State: Current situation of the agent
Reward: Feedback from the env.
Policy: Method to map agentโs state to actions
Value: Future reward that an agent would receive by taking an action in particular state
Letโs 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.
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.
The states are the location of PacMan in the grid world and the total cumulative reward is PacMan winning the game.
๐ฅ๐ฒ๐ฎ๐น-๐น๐ถ๐ณ๐ฒ ๐ฎ๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐
-Building AI for playing games.
-Robot Navigation
-Industrial Automation
-Dialog Agents(Text, Speech)
-Online Stock Trading
-Building AI for playing games.
-Robot Navigation
-Industrial Automation
-Dialog Agents(Text, Speech)
-Online Stock Trading
๐๐ผ๐ผ๐ธ ๐ณ๐ผ๐ฟ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฅ๐
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! ๐
๐ incompleteideas.net/book/the-book-โฆ
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! ๐
๐ incompleteideas.net/book/the-book-โฆ
๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป๐
-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.
-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 ๐
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 ๐
https://twitter.com/Sauain/status/1417072219272843264?s=20
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