What is the problem being addressed?
The paper addresses the problem of learning action models of black-box autonomous agents (both symbolic and simulator) through query answering.
Why is that a problem worth solving?

What is the problem being addressed?
The paper addresses the problem of exploring better generalizing policy representations mainly in the form of neural networks that can learn planning computations required for goal-directed behavior and solve general unseen tasks.

What is the problem being addressed?
The paper addresses the problem of speeding up the final convergence of RTDP, a heuristic search-DP algorithm to produce optimal policies for fully observable non-deterministic (more specifically, stochastic shortest path) problems faster compared to the existing search algorithms.

What is the problem being addressed?
This paper proposes a deep learning approach to learn generalized heuristic generation functions (HGFs) that can scale well to unseen problems with different object names and quantities without the use of symbolic action models.

What is the problem being addressed?
The authors address developing a framework to learn generalized domain-independent heuristics for planning without using any available heuristics. The goal is to learn heuristics that can generalize not only across problem instances of different sizes, states, and goals but also across unseen domains.

What is the problem being addressed?
The paper tackles the challenge of developing a general platform for experimenting with the choice of state-space search strategy, search algorithm, and heuristics when solving planning problems.