Reinforcement learning is one type of Machine learning. In a single sentence, in this learning process a machine learns using trial and error method. Here basically, we give the machine 2 instructions.
1. Try all possible ways.
2. From your experience avoid errors and increase success rate.
Suppose, we have a robot. There is a fire in front of it. The robot can do 2 things. Whether it can directly jump into the fire or run away from it.
At first it will try both ways. Jump into fire and fail. Then again it will run away and survive. The robot will remember it. Next time when it see the fire again, it will run away. This is the basic concept of reinforcement learning.
Reinforcement learning Algorithms:
- SARSA (State–action–reward–state–action)
- Relative value learning (R-Learning)
Where to apply:
There are many fields where we can apply it. Some examples are as follows:
Playing a game: Reinforcement learning can learn to play different games and can become master on it. One great example is “AlphaGo system”. Using this machine learning the system beat a high ranked Go player.
Natural language processing: Processing human language is very difficult task. By using it we are overcoming this issue.
Self driving car system: In the near future, we’ll see lot of self driving cars on the road. To make it come true reinforcement learning is contributing a lot. ML algorithms (e.g. Deep Q-Learning algorithm) are used in self driving car system to improve driving.
Robot’s movement: Robot’s different movements are improved over time by using reinforcement learning. For example, robot can grab an object more accurately by using this algorithm.
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