How Training AI Models In Simulated Environments Is Helping Researchers

Machine Learning Simulation Training

The entire AI training process occurs during the simulation and without needing to go through the tedious process of data collection that is both difficult and expensive.

While artificial intelligence is impacting the world in different ways, the capability of machine learning algorithms profoundly relies upon the data. Reinforcement learning specifically where the model learns from feedback from the environment. But it takes a lot of time and data to train a model, deploy in production which makes predictions in the real world.

So, in many instances, researchers working to train advanced AI/ML models are restricted by both the quality and the amount of data. So, on the off chance that you need to show a vehicle to drive itself, you will require a great many miles of human driving data. On the other hand, with the help of simulation just as humans do in their brains, researchers can create a great many training data sets and come up with innovative models.

There are many such systems working on simulation environments to help advance machine learning. One example is Facebook’s AI Habitat which enables training of embodied AI agents before transferring the learned skills to reality. This empowers a paradigm shift from ‘internet AI’ based on static datasets to embodied AI where agents act within realistic environments.

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Self Driving Is One Use For AI Training Models Via Simulated Environments 

Researchers have created models on autonomous vehicle fleet control in which vehicles self learn where they position themselves in a simulated environment. In such a model, each car is an agent and learns in an optimal way where different cars are moving in different positions. Amazon’s Aurora self-driving vehicle unit runs many simulations in parallel to train its models to explore urban conditions. The organization is preparing Alexa’s conversational resources, drones and robotic systems for logistics purposes across its fulfilment facilities. 

“We do not test what we can’t simulate to work. Simulation may fail to succeed, but if code doesn’t work in simulation, it likely won’t in the real world. We regularly test our code using unit tests, module tests and full system simulation tests before ever testing it on the road. Our objective is to launch self-driving vehicles securely and swiftly. Which means we prefer to craft models and learn their parameters rather than manually tune them. We prefer fast experiments. If we can have a prototype in Python, we try that first,” wrote The Aurora Team in a blog.

Reinforcement Learning In Conjunction With Simulation

An algorithm in order to devise an intelligent policy needs to experience a multitude of experience, running in millions of parameters. In a situation where there is a lack of data, simulation is what allows it to do that. Over the past few years, we have started seeing superhuman accuracy coming out of machine learning algorithms in a wide variety of complex problems. 

The entire AI training process occurs during the simulation and without needing to go through the tedious process of data collection that is both difficult and expensive. Subsequent to training, they deployed the learning inside the simulated environment onto the real-life scenario with great success. In 2019, models trained in recreated situations achieved accomplishments across more intricate and with many more parameters than past work.

The focus of much of the research has been games where algorithms are finding newer and more clever ways to win over humans- precisely the reason why past Go champions can’t even decipher how they are losing to DeepMind’s AlphaGo. Another example in Simulation & Reinforcement Learning is how DeepMind’s AlphaStar accomplished Grandmaster level in StarCraft II utilizing multi-agent reinforcement learning. Utilizing the advances depicted in its Nature paper, AlphaStar was positioned above 99.8% of players on

What this suggests is similar to how algorithms are solving incredibly complex problems in games (using simulation) can be extended to real-life complex problems, leading AI research companies have primarily focused on use cases like gaming and autonomous driving. This has to a lot of AI explainability and data privacy also where deploying AI models to solve complex problems in fields like medical sciences and businesses may be more challenging compared to gaming. 

Regardless, researchers have proposed use cases for using simulated models for solving business problems as well. For example, textiles which are heavily automation-based, researchers created a model to enhance the efficiency of the processes. Production processes were simulated to make regression from the time series data with machine learning. The errors that occur in the production process were created using random parameters in the simulation, and the variables showing the number of faulty products could be forecast successfully. 

Vishal Chawla
Vishal Chawla is a senior tech journalist at Analytics India Magazine and writes about AI, data analytics, cybersecurity, cloud computing, and blockchain. Vishal also hosts AIM's video podcast called Simulated Reality- featuring tech leaders, AI experts, and innovative startups of India.

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