Hey everyone. My name is Asta Johan. Welcome to the tutors Point
In the previous video, we have completed the unsupervised machine learning part with the video of association rule learning
With this video, we are going to have to start our reinforcement learning section
And in this particular video, we are going to learn about reinforcement learning itself
So let's see what's in for you. In this video, we're going to learn about what is reinforcement learning reinforcement learning with an ogy
What are the key components of reinforcement learning? And at the end
some concepts of reinforcement learning. First, let's understand what is reinforcement learning reinforcement learning is
a type of machine learning where an agent learns to behave by taking actions in an environment to achieve some goal
In the case of supervised learning, we were providing some labeled data to the model
And in the case of unsupervised learning, we were providing some unlabeled data to the model
But here in this case of reinforcement learning, we are not providing any labeled or unlabeled data to the model
but we have an agent here that take some actions in the given environment and learn based on the rewards or penalties
We have also discussed this example in first video of our playlist
So let's understand this example. Again, let's say we have this agent and we have provided some environment to the agent
Let's say the environment is a room and there are two points in the room
Point A and point B agent have to move from point A to point B
So let's say for every step that he takes closer to the point B will reward him plus 20 points
And for every step that he takes away from the point B
he will get minus 10 points. So for part one, let's say he get plus 60 points
And for part two, let's say he get plus 80 points. So for the next time when agent go through the room or the environment
he already knows that the part two is more efficient than the part one
So next time he will follow the steps that he take in the path to
Now, let's understand, reinforcement learning more deeply with an ogy, let's say we have this room and there is a baby
child in that room. So there are some candies in the room and flamed candles in the room
So let's say for the first case, this child crawls towards the candy
he reach to the candy and eats that candy. So he got some sweet taste
So he got happy feeling for that sweet taste. So in this case
he is getting some reward as that happy feeling. And let's say for the next case
he crawls down towards the candle and got attracted by the flame of the candle and he tries to hold that flame and got hurt
and he starts crying. So in that case, he's getting some penalty
So in future, whenever this child gets some candies, he will eat that
And if he sees some flame candles, he will avoid that. So just like humans learn from trial and error
this reinforcement learning algorithm also learns from trial and error. Now let's discuss some key components of the reinforcement learning
First one is agent, the learner or the decision maker is called the agent
Everything the agent interacts with is called the environment. All the possible moves the agent can take
are called actions a situation. The agent find itself uh within the environment is called the estate or state
of that agent. A feedback signal. After each action, the agent receives as a reward or penalty
which is a numerical score to learn what is good and bad
It's called reward. Now next is policy. Policy is a strategy that the agent employs to determine its next action
based on the current state. After that is value function, it estimates what the total amount of reward an agent can expect
to accumulate over the future starting from a specific state. Next is action value
Action value is similar to value except it takes extra parameter. That is the current action
Now here is a quiz for you all. And the question is how does a reinforcement learning agent receive feedback from its environment
And options are through a label data set provided at the beginning of training by comparing its action to those of pre
trained model through rewards or penalties after each action. And last option is via explicit instructions
from a human supervisor. I hope you all know the answer of this question as we have discussed about this just before
So write down your answers in the comment section. Now let's discuss some concepts of reinforcement learning
First one is reward maximization. It states that a reinforcement learning agent must be trained in such a way that
he takes the best action so that the reward is maximum. That means this agent will take some actions in this
environment in order to maximize his rewards. That means the more will be the rewards
the more will be the path efficiency. Next concept is exploitation. Exploitation is about using the already
known exploited information to heighten the rewards. That means this agent will take actions in this environment only
based on the information that he already knows. On the other hand
another concept is exploration, exploration is about exploring and capturing more information about the environment
That means in this case, agent will take actions in the environment in order to get more information to find all the possibilities
to get more rewards. And after that, he will decide what is the most efficient part
So let's say for the part one, he gets some plus 60 points
And for part two, he gets some plus 80 points if we talk about the exploitation case
So for the part three, he may take the part two, as he knows only two parts
part one and part two. In the case of exploration, he will find out new possibilities to maximize his reward
So let's say he find out all the possibilities and he found out a new path that is part three in which he is getting
some plus 100 points or plus 100 rewards. That's how exploration concept works
So that was it for this video. We have already covered the supervised machine learning algorithm part and unsupervised machine learning algorithm part
And with this video, we have started reinforcement learning algorithm part in which we are going to cover Q learning deep cue networks
policy gradient. So stay tuned with tutor point. Thanks for watching and have a nice day