Unsupervised pre-training has recently become the bedrock for computer vision and natural
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …

Humans excel in solving complex reasoning tasks through a mental process of moving from
one idea to a related one. Inspired by this, we propose Subgoal Search (kSubS) method. Its …

Abstract Hierarchical Reinforcement Learning (HRL) has made notable progress in complex
control tasks by leveraging temporal abstraction. However, previous HRL algorithms often …

Object packing by autonomous robots is an important challenge in warehouses and logistics
industry. Most conventional data-driven packing planning approaches focus on regular …

This paper addresses the problem of inventing and using hierarchical representations for
stochastic robot-planning problems. Rather than using hand-coded state or action …

The current state of the art in artificial intelligence is impressive, especially in terms of
mastery of language, but not so much in terms of mathematical reasoning. What could be …

This paper investigates the multi-agent navigation problem, which requires multiple agents
to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) has …

Hierarchical reinforcement learning (HRL) exhibits remarkable potential in addressing large-
scale and long-horizon complex tasks. However, a fundamental challenge, which arises …

Recently, graph-based planning algorithms have gained much attention to solve goal-
conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to …

Solving complex planning problems has been a long-standing challenge in computer
science. Learning-based subgoal search methods have shown promise in tackling these …