Sebastian Thrun, Knut Möller, Alexander Linden
We present a new connectionist planning method [TML90]. By interaction with an unknown environment, a world model is progressively construc(cid:173) ted using gradient descent. For deriving optimal actions with respect to future reinforcement, planning is applied in two steps: an experience net(cid:173) work proposes a plan which is subsequently optimized by gradient descent with a chain of world models, so that an optimal reinforcement may be obtained when it is actually run. The appropriateness of this method is demonstrated by a robotics application and a pole balancing task.