Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

Applying reinforcement learning (RL) to sparse reward domains is notoriously challenging
due to insufficient guiding signals. Common RL techniques for addressing such domains …

Entropy regularization is an important approach to improve exploration and enhance policy
stability for reinforcement learning. However, in previous study, entropy regularization is …

The deployment of unmanned aerial vehicles (UAVs) in ultra-dense networks (UNDs) has
significantly advanced network capabilities in 5G/6G environments, addressing coverage …

Reinforcement learning for hard-exploration tasks remains challenging due to the long-term
dependence and sparse-and-delay rewards in complex environments. In these challenging …

Conventional reinforcement learning (RL) algorithms often necessitate millions of
environment interactions to ascertain an efficacious policy. In stark contrast, humans …

Multi-agent reinforcement learning (MARL) effectively improves the learning speed of agents
in sparse reward tasks with the guide of subgoals. However, existing works sever the …