28,351 research outputs found
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
Improving the Parallel Execution of Behavior Trees
Behavior Trees (BTs) have become a popular framework for designing
controllers of autonomous agents in the computer game and in the robotics
industry. One of the key advantages of BTs lies in their modularity, where
independent modules can be composed to create more complex ones. In the
classical formulation of BTs, modules can be composed using one of the three
operators: Sequence, Fallback, and Parallel. The Parallel operator is rarely
used despite its strong potential against other control architectures as Finite
State Machines. This is due to the fact that concurrent actions may lead to
unexpected problems similar to the ones experienced in concurrent programming.
In this paper, we introduce Concurrent BTs (CBTs) as a generalization of BTs in
which we introduce the notions of progress and resource usage. We show how CBTs
allow safe concurrent executions of actions and we analyze the approach from a
mathematical standpoint. To illustrate the use of CBTs, we provide a set of use
cases in robotics scenarios
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