What is the problem being addressed?
The paper investigates methods for solving reinforcement learning while conforming to LTL restraining specifications, such that, the restraining bolt accounts for features of the world distinct from the RL agent.

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 authors address the problem of finding recursively optimal policies through methods for hierarchical reinforcement learning (HRL) that are general-purpose, support non-hierarchical execution, preserve the markovian property of subtasks involved, and are not adversely affected by state abstractions.

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.