How Useful is Reinforcement Learning ?

Introduction

Let us begin by explaining what reinforcement learning is all about.

Reinforcement learning is a part of Machine Learning (ML) that takes appropriate action to increase the reward in a specific situation. Numerous machines and software adopt reinforcement learnings to identify the most suitable action in a particular situation. It is different from supervised learning. The training data includes an answer key in a supervised learning system, so the model’s training happens with the right answers. However, there are no answers available in this system and the reinforcement agent chooses the appropriate action for the task given.

Key Points for Reinforcement Learning

• Input: The input should be the beginning state from which the model starts.
• Output: There are multiple probable outputs since numerous solutions are available for one specific problem.
• Training: The training of reinforcement learnings depends on the input. The model returns a state and the user has to decide if they can adopt the model based on its output.
• The model continues to learn.
• The selection of the solution is decided after understanding the different rewards.

How does reinforcement learning work?

In this environment, developers invent a method of rewarding the chosen behaviors and punishing negative behaviors. This method allocates positive values to the selected actions to inspire the agent. Negative values are allocated to the undesired behaviors. Continuous practice of this model programs the agent to pursue long-term and maximum overall reward to accomplish an ideal solution.

The long-term goals protect the agent from wasting time on goals that are not ideal. Thus, the agent learns to evade the negative and pursue the positive with time. This learning method has been adopted in Artificial Intelligence (AI) to lead unsupervised machine learning through penalties and rewards.

Examples and Applications of Reinforcement Learning

While reinforcement learning in machine learning has been a topic of significant interest in the field of AI, there has been limited adoption and application of reinforcement learnings in the real-world. However, there have been various successful use cases of reinforcement learnings. Current use cases include the following:

• resource management
• gaming
• robotics
• personalized recommendations

Reinforcement learning can function in any situation as long as a clear reward is applicable. In an Enterprise Resource Management (ERM), reinforcement learnings algorithms can assign limited resources to various tasks if there is a goal it is trying to achieve.

Common Reinforcement Learning Algorithms

It comprises several algorithms that take different approaches. Following are some of the most common reinforcement algorithms

• State-action-reward-state-action (SARSA): Begins by providing the agent a policy. The policy is a probability that specifies the odds of actions resulting in beneficial states or rewards.
• Q-learning: The agent receives no policy in this algorithm, meaning exploring its environment is more self-directed.
• Deep Q-Networks: These algorithms use neural networks with reinforcement learning techniques and the self-directed environment exploration of reinforcement learnings.

Closing Thoughts

Reinforcement learning has excellent potential; however, it can be challenging to deploy, limiting its application. One of the barriers for the deployment of reinforcement learnings in AI is its dependence on the exploration of the environment. For example, suppose one were to deploy a robot dependent on reinforcement learnings to travel through a complex physical environment. In that case, it will require new states and perform different actions as it moves. It is tough to perform the best actions in a real-world environment, as the environment changes frequently.