Planning Simple Trajectories Using Neural Subgoal Generators
Jürgen Schmidhuber, Reiner Wahnsiedler · The MIT Press eBooks · 1993
We consider the problem of reaching a given goal state from a given start state by letting an `animat' produce a sequence of actions in an environment with multiple obstacles. Simple trajectory planning tasks are solved with the help of `neural' gradient-based algorithms for learning without a teacher to generate sequences of appropriate subgoals in response to novel start/goal combinations. Relevant topic areas: Problem solving and planning, goal-directed behavior, action selection and behavioral sequences, hierarchical and parallel organizations, neural correlates of behavior, perception and motor control. 1 INTRODUCTION Many researchers in neuro-control and reinforcement learning believe that some `compositional' method for learning to reach new goals by combining familiar action sequences into more complex new action sequences is necessary to overcome scaling problems associated with non-compositional algorithms. The few previous ideas for attacking `compositional neural seque...