CONTINUOUS ACTION REINFORCEMENT LEARNING APPLIED TO VEHICLE SUSPENSION CONTROL

@inproceedings{Howell1997CONTINUOUSAR,
  title={CONTINUOUS ACTION REINFORCEMENT LEARNING APPLIED TO VEHICLE SUSPENSION CONTROL},
  author={M. N. Howell and Graham Frost and T. J. Gordon and WU Q.H.},
  year={1997}
}
A new reinforcement learning algorithm is introduced which can be applied over a continuous range of actions. The learning algorithm is reward-inaction based, with a set of probability density functions being used to determine the action set. An experimental study is presented, based on the control of a semi-active suspension system on a road going, four wheeled, passenger vehicle. The control objective is to minimise the mean square acceleration of the vehicle body, thus improving the ride… CONTINUE READING
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