In the era of technology, we’re all aware of how the importance of machine and reinforcement learning is increasing day by day. Machine learning is a method for detecting things through available data. In the past, we do proper programming to identify an aspect. But now, modern technology has shifted old strategies into new ones.
Following are the significant applications of machine learning;
- Netflix
- Amazon
- Identification of spams
There are three basics of machine learning. The first one is reinforced learning, while the rest is supervised and unsupervised learning. All of these basics are part of artificial intelligence. An interesting thing to note here is that these three basics have different roles to play. You would not find similarities in supervised and reinforced learning. Similarly, unsupervised and reinforced learning also differ from each other. This is because the purpose of reinforcement learning is to ensure the increase in the rate of reward for the agent.
Framing of Reinforcement Learning
According to academic experts of The Academic Papers UK, the following are the contributors of reinforcement learning;
- Agent
- Action
- Environment
- Reward
- Interpret
- State
Let’s understand the process of its working. First, the agent takes action and directs it towards the environment. In the second step, the environment interprets that action into two forms. This can be done in the form of a reward or state. Both reward and state are passed towards the agent again. That is how the whole cycle runs and how the process keeps working.
Before Woking on reinforcement learning, it is essential to know its framework. If this is not ensured, the chances of confusion increase. You can also make it more clear with some examples. Let’s take the example of a human. It would not be wrong to say that food is the basic need of every human. In the human body, the brain acts as an interpreter. Our hormones take action, and then our brain interprets it accordingly. Let’s suppose you’ve been starving for many hours. In this situation, your body feels down, and your blood pressure might also decrease due to a lack of food. Here your brain will interpret it as negative reinforcement learning. The reason behind this interpretation is that your body is under struggle and in a condition of pain.
On the other hand, when you eat something and feel no more need for food, you gain energy at that moment. Your body hormones react accordingly, and the brain interprets it as positive reinforcement learning. The reinforcement learning process in a machine works in the same way it does within a human body.
Real-Life Example of Reinforcement Learning
That time is not far when every person will use self-driving cars. The best use of reinforcement learning in future is also predicted through the advent of self-driving cars. There are many important aspects to note in driving, like speed, turns, stops, collisions, etc. A racing car is already tested for RL. And based on RL and its different aspects, it is assumed that self-driving cars will perform better than humans. There would be no involvement in human behaviour at all in this case.
The behaviour of humans plays an essential role in the collisions of cars. Whereas in the case of an automatic system, there would be no such factor of anger or short temperedness. For each movement, different sensors and actions are installed within the car.
Furthermore, robots are also the best application of reinforcement learning in future. Like self-driving cars, robots have different sensors that react accordingly. An algorithm is run for a robot’s functioning.
Environment
It is essential to understand the exact role of the environment in terms of reinforcement learning. Apart from this, you also have to decide the type of environment very wisely. For understanding the information about each type of environment, MDP is used. Within this context, the Markov decision process (MDP) is an ancient strategy, but its new roles in the environment are unique. As we just discussed the future scope of RL in all those examples, the Markov decision process (MDP) has its implementations. In MDP, the cycle runs between the action and state. As the action varies, the state change is evident. With each set of actions and state, corresponding rewards are observed. There are many mathematical forms available for MDP. And as per each expression, the probability of action is calculated.
Breakdown of Environment
As discussed above, the environment is an element in reinforcement learning. In the whole framework, the leading role is that of the environment. This is because it decides if the selected strategy is best suitable for the agent or not. Its environment gives feedback to the agent, and through which the agent changes strategies according to the feedback.
The environment has different sets of activities that are as follows;
- Discrete Action Space
- Continuous Action Space
Based on these sets of action space, the categories of the environment in reinforcement learning are designed. These categories include the following domains;
- Deterministic Environment
- Stochastic Environment
- Single-Agent Environment
- Multi-Agent Environment
- Discrete Environment
- Continuous Environment
- Episodic Environment
- Sequential Environment
- Fully Observable Environment
- Partially Observable Environment
Deterministic Environment vs Stochastic Environment
In the deterministic environment type, it is easy to assume the future actions of the environment for an agent. For example, if I move left, it is obvious to turn left. Whereas in a stochastic environment, the next movement is always doubtful. You cannot assume anything about it.
Single Agent Environment vs Multi-Agent Environment
As the name suggests, the number of agents varies in these types of environments. In a single-agent environment, you have only one agent to operate the system. Whereas in a multi-agent environment, two or more agents are introduced as per the demand of the hour.
Discrete Environment vs Continuous Environment
In discrete and continuous environments, the nature of action is finalised. For a continuous environment, the best example is a self-driving car. In this aspect, the speed and all other rotating systems are controlled using a continuous environment.
Like these types of environments, all others have specified roles.
Final Thoughts
The scope of reinforcement learning is very bright. Artificial intelligence is a never-ending field where RL works as a subset of AI in this context. Also, no matter what the working area of RL is, an understanding of all its elements is necessary. This is because the leading element in RL is the environment that decides results.