Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Data centers are experiencing unprecedented growth as the fourth industrial revolution's
supporting pillars and the engine for the future digitalized world. However, data centers are …

Long-horizon tasks, usually characterized by complex subtask dependencies, present a
significant challenge in manipulation planning. Skill chaining is a practical approach to …

This article proposes a hybrid policy-based reinforcement learning (HPRL) adaptive energy
management to realize the optimal operation for the island group energy system with energy …

Despite the potential of reinforcement learning (RL) for building general-purpose robotic
systems, training RL agents to solve robotics tasks still remains challenging due to the …

Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …

Recent work has shown that deep neural networks are capable of approximating both value
functions and policies in reinforcement learning domains featuring continuous state and …

In recent years, the importance of electric mobility has increased in response to climate
change. The fast-growing deployment of electric vehicles (EVs) worldwide is expected to …

Most existing deep reinforcement learning (DRL) frameworks consider either discrete action
space or continuous action space solely. Motivated by applications in computer games, we …

Automating molecular design using deep reinforcement learning (RL) holds the promise of
accelerating the discovery of new chemical compounds. Existing approaches work with …