Reinforcement Learning (RL) is crucial for data-driven decisionmaking but suffers from
sample inefficiency. This poses a risk to system safety and can be costly in real-world …

While deep reinforcement learning (RL) is becoming an integral part of good decision-
making in data science, it is still plagued with sample inefficiency. This can be challenging …

Recent advancements in artificial intelligence have enabled reinforcement learning (RL)
agents to exceed human-level performance in various gaming tasks. However, despite the …

Reinforcement learning (RL) algorithms can successfully solve a wide range of problems
that we faced. Because of the Alpha Go against KeJie in 2017, the topic of RL has reached …

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

Reinforcement learning (RL) still suffers from the problem of sample inefficiency and
struggles with the exploration issue, particularly in situations with long-delayed rewards …

Many continuous control tasks have easily formulated objectives, yet using them directly as
a reward in reinforcement learning (RL) leads to suboptimal policies. Therefore, many …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Reinforcement learning (RL) aims to learn an optimal control by interacting with the
environments. Reinforcement learning, especially deep reinforcement learning, has …

Recent methods using Reinforcement Learning (RL) have proven to be successful for
training intelligent agents in unknown environments. However, RL has not been applied …