Related papers: Robust Hierarchical Planning with Policy Delegation

Robust Hierarchical Planning with Policy Delegation

URL: http://arxiv.org/abs/2010.13033v1
Date: Sun, 25 Oct 2020 04:36:20 GMT
Title: Robust Hierarchical Planning with Policy Delegation
Authors: Tin Lai, Philippe Morere
Abstract summary: We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation. We show this planning approach is experimentally very competitive to classic planning and reinforcement learning techniques on a variety of domains.
Score: 6.1678491628787455
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation. This framework, the Markov Intent Process, features a collection of skills which are each specialised to perform a single task well. Skills are aware of their intended effects and are able to analyse planning goals to delegate planning to the best-suited skill. This principle dynamically creates a hierarchy of plans, in which each skill plans for sub-goals for which it is specialised. The proposed planning method features on-demand execution---skill policies are only evaluated when needed. Plans are only generated at the highest level, then expanded and optimised when the latest state information is available. The high-level plan retains the initial planning intent and previously computed skills, effectively reducing the computation needed to adapt to environmental changes. We show this planning approach is experimentally very competitive to classic planning and reinforcement learning techniques on a variety of domains, both in terms of solution length and planning time.

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