Update index.md (#23882)

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The best example, and one which you will hear a lot in this field, is AlphaGo developed by Google. This uses reinforcement learning to learn the patterns, rules and semantics of the board game, Go. This bot defeated the World No. 1 Go player, Lee Sedol, in what was the first time a computer program defeated a professional player. AlphaGo won by 4-1 in a five game series. This was a huge victory for AI and kickstarted the field of Reinforcement learning.
## List of Common Algorithms
Q-Learning
Temporal Difference (TD)
Deep Adversarial Networks
1. Temporal Difference (TD)
* Q-Learning
* SARSA
2. Policy Gradient and Actor-Critic Methods
* A3C
* DDPG
* REINFORCE
## Use cases:
Some applications of the reinforcement learning algorithms are computer played board games (Chess, Go), robotic hands, and self-driving cars.
## More information:
* [David Silver's RL course](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
* [UC Berkeley's RL course](http://rail.eecs.berkeley.edu/deeprlcourse/)
* [RL using Tensorflow](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0)