Using DRL in robotics has become easier since Google published its Soft Actor Critic algorithm. This algorithm helps robots use reinforcement learning to learn real-world tasks without the need for huge datasets or a large number of attempts, and while safeguarding the robot from taking actions that could cause it damage. For example, the algorithm has thus far taken a few hours in successfully training an insect robot to walk and a robot hand to carry out simple tasks.

DRL in Robotics

DRL can be employed in healthcare due to the large amount of historical medical data to evaluate the success of treatments for current patients. For example, DRL is used to predict drug doses for sepsis patients, for finding optimal dose cycles for chemotherapy, and selecting dynamic treatment regimes combining hundreds of possible medications based on medical registry data.

DRL Healthcare Applications

DRL has been used to optimize optimize chemical reactions, outperforming an existing state-of-the-art algorithm used to conduct the same experiment. A reinforcement learning agent successfully optimized a sequential chemical reaction, predicting at every stage of the experiment which action would generate the most desirable chemical reaction.

DRL in Chemistry

Examples of DRL use in real-world environments can be found in:

1) Robotics

2) Healthcare Applications

3) Chemistry

University of Michigan - Ann Arbor

Deep Reinforcement Learning (DRL) is data inefficient and may require large amounts of data through millions of iterations to give results for simple tasks. To circumvent this, simulated environments are often used to train DRL models. There are some major practical gaps between simulated and real environments and this makes the models difficult to operate feasibly in the real-world. This is why DRL is used widely in closed environments like video games, but is difficult to apply in real-world environments. Some organizations use this by opting for a deep learning platform (platforms with large data sets and documentation easily available) to help them implement DRL.

Using DRL in Real-World Environments

https://missinglink.ai/guides/neural-network-concepts/complete-guide-deep-reinforcement-learning-concepts-process-real-world-applications/

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