Machine Learning is one awe-inspiring technology that has got everyone talking! As a result, researchers from all across the planet are trying to know more and better about this domain.
ML is a subset of AI (Artificial Intelligence). Where AI refers to any kind of intelligent machine, ML, on the other hand, refers to a particular type of AI that learns by itself!
ML is precisely what its name is – when machines learn, i.e., the science of getting computers to act without explicitly programming them. Instead, it focuses on using algorithms and data to enable machines to learn in a way humans do. One paradigm of this technology is RL – Reinforcement Learning.
There are two other paradigms, but we will stick with RL for today’s piece and learn more about this wonderful solution. So, let’s get to the fundamentals already!
First things first – what is RL?
Since it is a technical and scientific topic, we can’t move without actually understanding what RL is; so, it is only mandatory to discuss the basic definition first!
Reinforcement Learning can be seen as the Freudian approach to psychology – about rewarding and punishing the subject to get the desired result. RL is precisely that but with machines.
It is defined as a machine learning method that enables an agent to learn through trial and error in an intelligent environment. It involves rewarding or penalising the artificial intelligence for the actions it performs. For instance, if the machine does what the programmer wants, it is rewarded and if it doesn’t, it is penalised. The ultimate goal is to maximise awards!
This neural network method helps to learn ways to attain complex objectives over many steps.
Terms you must know about
There are few terms that one must be aware of when dealing with RL. They can be considered the scientific jargons related to this domain. Let us get to know them!
- Environment (e) – the scenario that the agent faces.
- Model of the environment – it mimics behaviour of the environment.
- Model based methods – a method used for solving reinforcement learning problems.
- State (s) – present situation returned by the environment.
- Agent – the entity that performs actions in an environment to get rewards.
- Reward (R) – an immediate gift given to the agent on performing a specific task.
- Policy (π) – the strategy of the agent to decide its next action, based on the current status.
- Value (V) – the expected long-term return with discount.
- Value Function (V) – specifies the value of a state that is the total amount of reward.
- Q value or action value (Q) – it is similar to value, but it takes an additional parameter as a current action.
Rendezvous with RL algorithms
It is a value-based, model-free, off-policy learning algorithm. Here, the agent receives no policy. This means that the exploration of an agent of its environment is self-directed!
- Deep Q-Networks
This algo utilises neural networks and RL techniques. Here self-directed exploration of the RL environment. The future actions are determined by a random sample of past beneficial action.
- PPO (Proximal Policy Optimisation)
The PPO algorithm was introduced in 2017 and quickly usurped the Deep-Q learning method. This involves collecting a batch of environment interaction experiences and then updating the decision-making policy!
SARSA stands for State-Action-Reward-State-Action. It is an algo for learning Markov decision process policy. It starts by giving the agent a policy and is an ON-policy algorithm for TD-learning (Temporal Difference).
DDPG stands for Deep Deterministic Policy Gradient and is a model-free off-policy. It combines ideas from DQN and DPG and is an algo for learning continuous actions.
The other two paradigms of ML
As mentioned earlier, RL isn’t the only tech falling under the ambit of ML. There are two other learnings (so to say). Let us get to know about them, before wrapping up! Goes without saying that both the following are a subset of AI and ML.
Here, machines are trained using well-labelled training data – something that SL, as a name, signifies. On the basis of this training data, the machines then predict the outcome/output.
SL involves providing input data as well as correct output data to the ML model. The aim is to map the input variable with the output variable.
As the name suggests, it does not involve any supervision and the models are not supervised using training dataset. The models themselves find the hidden patterns from the data given. UL algorithms first self-discover any naturally occurring patterns in a dataset and thus it is considered more important.
It is so because in real-world, input data with a corresponding output isn’t always available. Therefore, to solve such cases, unsupervised learning is required.
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Reinforcement learning, without a doubt, is a cutting-edge technology that has a lot to offer in the future. Since it falls under the ambit of Machine Learning, experts of the latter can help build RL-solutions and work on the related algorithms.
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