Reinforcement Learning – From Playing Games to Driving Cars


Computers are making use of a seemingly simple approach called reinforcement learning to not just excel at complex games but making driverless cars possible too.

What is Reinforcement Learning?

It is an approach to artificial intelligence that gets computers to learn like people by doing things incrementally well, without explicit instruction. The author gets a taste of this at a Barcelona AI conference where self-driving cars in a simulation learn to maneuver through a complicated four-lane highway by practicing moves over and over. This is also the approach that helped AlphaGo, designed by Alphabet subsidiary DeepMind beat the World No. 1 Go player in 2017 – Go is a notoriously complex game, making this achievement particularly remarkable.

Reinforcement Learning – A Historical Perspective

Psychologist Edward Thorndike first documented this principle over 100 years ago with an experiment involving cats who learned to escape from a box after stepping on a lever by chance. An early reproduction in machines was in 1951, where a machine simulating a rat escaping from a virtual maze saw some synaptic connections strengthen to reinforce successful behavior. Following decades saw intermittent successes, but using this for more complex tasks became computationally impractical.

The Breakthrough of Deep Learning

In recent years, reinforcement learning has become so formidable because of breakthrough in deep learning, which uses a large simulated neural network to recognize data patterns. This makes storing values for every move and updating them – the basis of reinforcement learning – easier.

This breakthrough is also what made DeepMind first excel phenomenally in Atari video games in 2013, resulting in its acquisition by Google. Today, Google is using these capabilities to make its data centers more energy efficient.

Reinforcement Learning in Self-Driving Cars

This approach is particularly well-suited for simulating humanlike behavior in self-driving cars. Today’s driverless cars often falter in complex situations that involve interacting with human drivers, such as traffic circles. To prevent extreme behavior – taking unnecessary risks or being too hesitant – these cars will have to acquire nuanced skills.

Mobileye, the Israeli firm that developed the Barcelona demo, plans to test the software on a fleet of vehicles with BMW and Intel this year. Google and Uber are already testing reinforcement learning for their vehicles.

While reinforcement learning helps automated driving by enabling good sequences of decisions, which is more efficient than pre-programming all such decisions, there are challenges too. The huge amount of data, the time needed to practice simulations, multiple objectives (avoiding accidents vs. keeping roads safe for other cars) – all make this an extremely complex area.

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