This article surveys reinforcement learning approaches in social robotics. Reinforcement
learning is a framework for decision-making problems in which an agent interacts through …

Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems,
trains an agent to achieve different goals under particular scenarios. Compared to the …

Goal-oriented reinforcement learning has recently been a practical framework for robotic
manipulation tasks, in which an agent is required to reach a certain goal defined by a …

As an advanced combat weapon, Unmanned Aerial Vehicles (UAVs) have been widely
used in military wars. In this paper, we formulated the Autonomous Navigation Control …

Multi-robot environmental coverage can be widely used in many applications like search
and rescue. However, it is challenging to coordinate the robot team for high coverage …

Learning with sparse rewards remains a challenging problem in reinforcement learning
(RL). In particular, for sequential object manipulation tasks, the RL agent generally only …

In multigoal reinforcement learning, an agent interacts with an environment and learns to
achieve multiple goals. The goal-conditioned policy is trained to effectively generalize its …

Building embodied agents on integrating Large Language Models (LLMs) and
Reinforcement Learning (RL) have revolutionized human-AI interaction: researchers can …

The aim of path planning is to search for a path from the starting point to the goal. Numerous
studies, however, have dealt with a single predefined goal. That is, an agent who has …

The sparse reward problem stands as a significant challenge in the field of reinforcement
learning. Hindsight Experience Replay (HER) addresses this by goal relabeling, allowing …