Neural planners for RDDL MDPs produce deep reactive policies in an offline fashion. These
scale well with large domains, but are sample inefficient and time-consuming to train from …

Abstract Domain-independent probabilistic planners input an MDP description in a factored
representation language such as PPDDL or RDDL, and exploit the specifics of the …

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 …

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 …

Recent advances in reinforcement learning (RL) have led to a growing interest in applying
RL to classical planning domains or applying classical planning methods to some complex …

In this paper, we discuss the learning of generalised policies for probabilistic and classical
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …

In this paper, we introduce the Action Schema Network (ASNet): a neural network
architecture for learning generalised policies for probabilistic planning problems. By …

Recent advances in applying deep learning to planning have shown that Deep Reactive
Policies (DRPs) can be powerful for fast decision-making in complex environments …

Planning, the ability to analyze the structure of a problem in the large and decompose it into
interrelated subproblems, is a hallmark of human intelligence. While deep reinforcement …

Neural algorithmic reasoning studies the problem of learning algorithms with neural
networks, especially using graph architectures. A recent proposal, XLVIN, reaps the benefits …