The core of Reinforcement Learning
By the core of reinforcement learning is the notion that the ideal action or behavior is buttressed by a positive reward.
In the same way, as toddlers learn how to walk, adjusting their actions on the consequences they encounter like taking a smaller step if the previous wide step resulted in falling, software agents and machines use reinforcement learning algorithms to establish the idyllic behavior according to the feedback received from the environment. It is a type of machine learning and thus a branch of artificial learning.
Subjected to the intricacy of the glitch, reinforcement learning algorithms keep acclimatizing to the environment as time passes by to maximize the reward in the long-run. So, like the doddering toddler, a robot who is just learning to walk with reinforcement will always try different ways to accomplish the objective, get an opinion about how positive those ways are and then adapt until the goal to walk is achieved.
A broad step forward will make the robot fall, so it tweaks its step to make it smaller to see if that trick beholds the secret to stay upright and continues its learning through different combinations till it is finally able to walk. In this situation, the punishment is falling, and the reward is staying upright. The robot understands this by the feedback it receives. Therefore, optimal actions getting reinforced.
This technique requires a huge amount of data, this is the reason why the initial applications for the technology were introduced in areas where fake data is available such as in robotics and gameplay.
Practical Examples of Reinforcement Learning
Although we are in the embryonic stage of reinforcement learning, several products and applications have already started to rely on this technology. Slowly and steadily companies have started to employ reinforcement learning for problems that require sequential decision-making where reinforcement learning is able to support automated decision-making process or human experts. Here are a few examples:
Reinforcement learning provides robotics with a set of tools and a framework for difficult-to-engineer behaviors. As reinforcement learning is possible without supervision, this could aid in the exponential growth of robotics.
- Industrial automation
Owing to the reinforcement learning potentials from DeepMind, Google successfully reduced energy consumption in its data centers. Recently acquired by Microsoft, Bonsai offers reinforcement learning to mechanize and “build intelligence into complex and dynamic systems” in energy, HVAC, manufacturing, automotive and supply chains.
- Enhance predictive maintenance
Machine learning has been used in manufacturing for some time, but reinforcement learning would make predictive maintenance even better than it is today.
Reinforcement learning is ideally suited to figuring out optimal treatments for health conditions and drug therapies. It has also been used in clinical trials as well as for other applications in healthcare.
- Autonomous vehicles
Most autonomous cars, trucks, drones, and ships have reinforcement algorithms at the center. Wayve, a UK company, designed an autonomous vehicle that learned to drive in 20 minutes with the help of reinforcement learning.
As substantial data is required to make reinforcement learning work, more and more companies will be able to control reinforcement learning capabilities as they keep on acquiring more data. Since the value of reinforcement learning keeps on growing, companies will continue investing in resources to find the best way to apply the technology in their products, operations, and services.