With the widespread use of reinforcement learning (RL) in the energy system, the damage to
the system caused by the agents' stochastic exploration is beginning to be appreciated. We …

Reinforcement learning (RL) is a potential method for tackling difficult decision-making
problems in power and energy systems. This review paper presents a thorough examination …

To mitigate global climate change and ensure a sustainable energy future, China has
launched a new energy policy of achieving carbon peaking by 2030 and carbon neutrality …

Agent exploration of reinforcement learning is a necessary way for reinforcement learning
algorithms to obtain information. In order to obtain more exploratory information, some deep …

Due to the availability of more comprehensive measurement data in modern power systems,
there has been significant interest in developing and applying reinforcement learning (RL) …

Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

The large-scale integration of intermittent renewable energy resources introduces increased
uncertainty and volatility to the supply side of power systems, thereby complicating system …

Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal
control direction for multi-energy management systems. It only requires the environment …

With the growing integration of distributed energy resources (DERs), flexible loads, and
other emerging technologies, there are increasing complexities and uncertainties for …

Reinforcement learning (RL) is a promising optimal control technique for multi-energy
management systems. It does not require a model a priori-reducing the upfront and ongoing …