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We improve inverse reinforcement learning(IRL) by applying dimension reduction methods to automatically extract Abstract features from human-demonstrated policies,to deal with the cases where features are either unknown or numerous.The importance rating of each abstract feature is incorporated into the reward function.Simulation is performed on a task of driving in a five-lane highway,where the controlled car has the largest fixed speed among all the cars.Performance is almost 10.6% better on average with than without importance ratings.
We improve inverse reinforcement learning (IRL) by applying dimension reduction methods to automatically extract Abstract features from human-demonstrated policies, to deal with the cases where features are either unknown or numerous. The importance rating of each abstract feature is incorporated into the reward function . Simulation is performed on a task of driving in a five-lane highway, where the controlled car has the largest fixed speed among all the cars. Performance is almost 10.6% better on average with than without rating ratings.