Training Deep Reactive Policies for Probabilistic Planning Problems
By Murugeswari Issakkimuthu, Alan Fern, Prasad Tadepalli. 2018
42 citations recorded by the source corpus.
State-of-the-art probabilistic planners typically apply look- ahead search and reasoning at each step to make a decision. While this approach can enable high-quality decisions, it can be computationally expensive for problems that require fast decision making. In this paper, we investigate the potential for deep learning to replace search by fast reactive policies. We focus on supervised learning of deep reactive policies for probabilistic planning problems described in RDDL. A key challenge is to explore the large design space of network architectures and training methods, which was critical to prior deep learning successes. We investigate a number of choices in this space and conduct experiments across a set of benchmark problems. Our results show that effective deep reactive policies can be learned for many benchmark problems and that leveraging the planning problem description to define the network structure can be beneficial.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Blai Bonet
- Hector Geffner
- Leliane Nunes de Barros
- Aniket Bajpai
- Scott Sanner
- Mausam
- Denis Deratani Mauá
- Sankalp Garg
Citations in the corpus
- Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, background): “More recently, general policies have been learned using deep learning methods but also starting with PDDL models [36, 9, 22, 6].”
- Deep Reactive Policies for Planning in Stochastic Nonlinear Domains (2019, background): “Currently, most works on DRPs in model-known planning have focused on discrete stochastic policies (Issakkimuthu, Fern, and Tadepalli 2018; Toyer et al. 2018; Groshev et al. 2018).”
- Transfer of Deep Reactive Policies for MDP Planning (2018, methodology): “One work learns deep reactive policies by using a network that mimics the local dependency structure in the RDDL representation of the problem [Fern et al., 2018].”