When AI comes into conversations, it is not unusual for discussions to involve the concept of deep reinforcement learning. Many believe it is promising when addressing most of the shortcomings of machine learning (ML), mainly processing raw inputs.
What Is Deep Reinforcement Learning?
Deep reinforcement learning is a subfield of ML and AI where intelligent machines learn from their actions akin to how humans learn from past experiences. It is a goal-oriented algorithm that can learn via trial and error.
Note, though, that deep reinforcement learning differs from supervised and unsupervised ML. In supervised learning, a machine predicts labels to use for complex inputs. In unsupervised learning, meanwhile, a system groups related items to improve outcomes. Reinforcement learning is capable of predicting actions to produce the best possible results.
How Does Deep Reinforcement Learning Work?
Deep reinforcement learning works, as mentioned above, by trial and error. How?
A machine is fed continuous streams of data. But unlike ordinary ML-enables systems, its learning is reinforced. The system’s training is strengthened by ensuring that it receives a reward or penalty based on its action. If it produces a desired outcome, it gets a reward, thus reinforcing its course of action. If it fails, it gets penalized by being made to try again. Over time, the rewards are reduced to encourage the system to keep finding the best solution either for the short or long term. This process makes deep reinforcement learning most applicable in dynamic environments.
The concept of reinforcement learning existed long before AI came to the fore. But its combination with deep learning allowed tech experts to yield excellent results. In deep reinforcement learning, “deep” refers to several layers of artificial neural networks that replicate the human brain’s structure.
What Are the Real-World Applications of Deep Reinforcement Learning?
Massive volumes of data and significant computing power are required to handle all deep reinforcement learning processes. Thankfully, supercomputers have tremendously increased computing power while continually reducing costs, enabling the concept to expand.
Robotics
Deep reinforcement learning is crucial in streamlining the learning process of robots. With it, robots can learn to do real-world tasks without undergoing several attempts. Google recently published its Soft Actor-Critic (SAC) algorithm, which uses deep reinforcement learning, which taught robots within hours while safeguarding them from carrying out actions that may be potentially damaging.
Healthcare
Deep reinforcement learning can also improve healthcare. It allows machines to go through tons of historical medical data to see which treatments produced the best results. That enables them to predict which treatments may be used for other patients.
In one particular study, a deep reinforcement learning-enabled computer predicted the drug dosage to use for a patient suffering from sepsis, identified the optimal dose for chemotherapy, and chose a dynamic treatment regimen by consolidating hundreds of medications from historical medical data.
Manufacturing
Intelligent robots are now used in many warehouses and manufacturing hubs. These robots are often tasked to sort millions of products for delivery. Deep reinforcement learning helps improve this process.
Deep reinforcement learning can help the robot pick the right device to place in a container. It can also assess if the robot did the job correctly and use this knowledge to improve future processes.
Automotive
Deep reinforcement learning can also transform automobile factories, improve vehicle maintenance, and enhance overall process automation. Since the automotive industry relies heavily on safety and quality while reducing costs, deep reinforcement learning can gather data from customers, dealers, and warranties to maintain product quality.
Bots
Deep reinforcement learning is also applied to AI bots, particularly in conversational user interface (UI) paradigms. With it, bots quickly learn about semantics and language nuances to improve automated speech processing and natural language understanding.
Since deep reinforcement learning allows machines to learn from interactions with their environment, many other applications can be further developed.
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Now that you know the answer to “What is deep reinforcement learning?” you are probably excited about the future possibilities the technology brings. Thankfully, we now see AI toolkits like DeepMindLab, OpenAI Gym, and Psychlab, which provide a training environment to jumpstart large-scale deep reinforcement learning innovation. As more organizations turn to the technology and with more available resources at hand, we are bound to see deep reinforcement learning grow even more.
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