Reinforcement Learning is a machine learning framework that enables an agent to evaluate the current environment, take optimal action, and get feedback from the environment after each step to maximize returns. RL is generally formed as a Markov Decision Process, where optimization is achieved in scenarios where decision making is done with partial control of a decision maker.
Markov Decision Process involves a method in a given state where a decision maker choose an available action, and the outcome of the process involves a random movement to a new state as well as a reward to the decision maker.
Factors to consider before considering reinforcement learning
To apply RL to a problem, a few decisive conditions need to be met, such as:
Scope of experimentation: The problem must allow scope for the system to perform a trial and error scenario.
Reward mechanism: The system must get rewards as a motivator to proceed.
Application of MDP: The problem must fit in the definition of a Markov Decision Process.
Core authority: The problem must involve a body which can independently perform the action and learning.
Simulation: Because of the iterative nature of RL problems, the simulations must be available before an RL algorithm can learn an optimum solution.
The real-world value of Reinforcement Learning
The strength of RL algorithms is being applied in solving various business scenarios in the real world where task automation is required.
Manufacturing: Manual tasks of manufacturing which usually require tremendous labor hours and human efforts are performed with automated robots with high accuracy and speed. A Japanese company named Fanuc manufactures robots who can self-learn for a broader range of industries. The robots made by this company can pick the right objects out of a box with few annotations and sensor technology, thus lessening the training efforts drastically.
Resource Optimization: Creating solutions for resource management tasks such as allocating computers to several awaiting jobs can be challenging, requiring human intervention. RL algorithms can be effectively used to learn about the vacancy and allocate resources to the waiting jobs, resulting in less delay.
Auto-configuration for web systems: Due to the dynamic trait of internet traffic, the configuration of the web system is a crucial aspect with regards to speed and performance. Reinforcement learning approach can achieve automatic configuration by auto-adapting performance parameter settings as per changing workloads as well as virtual configurations. This approach can be enhanced with an effective initiation which can reduce the learning time for the web systems.
Personalized news recommendations: Personalized news recommendation is usually a challenging problem due to the dynamicity and unpredictable user preferences. The current recommendation methods have many limitations in terms of lack of accuracy and user engagement. The RL approach can model a recommendation framework which can predict future rewards with more clarity with respect to user feedback.
Real-time bidding and advertising: Real-time bidding and advertising usually require an accurate connection between ads and users’ preference as well as strategic placements with respect to other advertisers. A multi-agent RL approach involving a clustering can be used here where each cluster can be assigned as a strategic bidding agent. The cluster-based mechanism can be more effective than the single-agent approach as the collaborated bidding achieves better objective than the independent bidding agents.
The value of reinforcement learning is going to be tremendously vital due to its automation capabilities. In the future, RL is going to bridge the gap between ideas and realities in terms of business value as well as human capital management.
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