The Conference for Machine Learning Innovation

Leveraging Reinforcement Learning for Improvement of Traditional Controllers for a Vehicle Model – A Case Study

Session
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✓ Special discount for freelancers
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Register until conference starts:
✓ 2 in 1 conference special
✓ 10 % Team Discount
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Infos

We introduce a case study where the goal was to improve the performance of an existing PIcontroller for a vehicle simulation on an oval circuit, using a Reinforcement Learning (RL) agent. It is an example of applying RL to solve a realistic (and complex) problem. Typically, there are two main challenges associated with a control-oriented RL problem: Finding a suitable network architecture for the RL agent, and designing an accurate reward function that can efficiently guide the agent during training and help minimize the training time, which is a well-known RL bottleneck. We will describe our solution approach in detail as well as the problems associated with training the RL agent. The results indicate that the RL agent can be used to outperform a strategy which employs only a traditional controller. In addition to this, our case study also contains an example of applying imitation learning.

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Munich Munich .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Singapore Singapore .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Berlin Berlin .

This Session Diese Session originates from the archive of stammt aus dem Archiv von MunichMunich . Take me to the current program of . Hier geht es zum aktuellen Programm von Munich Munich , Singapore Singapore or oder Berlin Berlin .

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