Today, one of the most intriguing areas of Artificial Intelligence (AI) is the conception of deep reinforcement learning – where machines can train themselves based on the outcomes of their actions, like how humans learn from experience. Intrinsic in this type of machine learning is that the agents get a reward or penalized based on their actions, leading them to the target outcome. In essence, deep RL merges artificial neural networks with a reinforcement learning architecture that enables software-defined agents to absorb the best possible actions in a virtual environment to achieve their goal; and, this distinctive area of AI shows potential for a promising future in the tech world.
A data-driven paradigm for deep reinforcement learning allows to pre-deploy agents, with the aptitude of sample-efficient learning in the real-world. The “deep” part of reinforcement learning indicates many layers of artificial neural networks that imitate the human brain’s structure. In domains, such as autonomous driving, robotics, and games, deep learning requires a massive volume of training data and an immense computing power. Over the last few years, Deep learning Frameworks have given the edge to business, and deeper volumes of data have escalated while the computing power cost has shrunk, enabling the explosion of deep reinforcement learning applications.
Applications of Deep Reinforcement Learning
Let’s have a look at applications of deep reinforcement learning!
The automotive industry has a diverse and huge dataset that overpowers deep reinforcement learning. Already in use for autonomous vehicles made by Uber or Tesla, it will assist in transforming factories, maintaining vehicles, and inclusive automation in the industry. The industry is being driven by quality, cost, and safety; and DRL with data from patrons and dealers will offer new opportunities to strengthen the quality, reduce cost, and have a higher safety record.
Some pre-eminent AI toolkits including OpenAI Gym, Psychlab, and DeepMind Lab offer the training environment that is intrinsic to hurl large-scale innovation for deep reinforcement learning – these open-source tools have the ability to train DRL agents. The more organizations adapt deep reinforcement learning to their unique business use cases, the more we will be able to witness a large increase in practical applications.
Intelligent robots are becoming more commonplace in warehouses and fulfillment centers to sort out umpteen products along with delivering them to the right people. When a device is being picked by a robot to put in a container, deep reinforcement learning assists it to wise up and use this knowledge to perform more in the future.
Whether it is about the optimal treatment plans or the new drug development and automatic treatment, there exists a great potential for deep reinforcement learning to advance in healthcare. As per now, one of the chief deep RL applications in healthcare includes the diagnosis of diseases, drug manufacturing, and clinical trial & research.
The conversational UI paradigm, making AI bots possible leverages the power of deep reinforcement learning. The bots are learning the semantics and nuances of language in various domains for both natural language and automated speech understanding; thanks to deep reinforcement learning!
Deep RL is using to make complex interactive video games – the RL agent’s behavior vicissitudes based on its learning from the game to optimize the score. It is also used in PC games such as ‘Chess’ where the opponents change their approach and move based on the player’s performance.
There is so much more when it comes to the potential for deep reinforcement learning. Since this sector of AI learns by interacting with its environment, the possible applications have no limitations. Thus, in this blog, we have shown some of the deep RL applications’ instances in various industries. If you’re a decision-maker of a company, then this blog is adequate to induce you to rethink your business and observe it yourself if you can use RL. Although RL still has different foibles, it also means it has plenty of research opportunities and a great potential to improve quality of life.