Deep Reinforcement Learning (DRL) aims to create intelligent agents that can learn to solve
complex problems efficiently in a real-world environment. Typically, two learning goals …

Reinforcement learning (RL) has been shown to be an effective mechanism for learning
complex tasks via interaction with the environment. Recent advances in combining deep …

Reinforcement learning has achieved great success in various applications. To learn an
effective policy for the agent, it usually requires a huge amount of data by interacting with the …

Deep reinforcement learning (DRL) has achieved remarkable results in many high-
dimensional continuous control tasks. However, the RL agent still explores the environment …

Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision-
making and continuous control tasks. However, applying RL algorithms on safety-critical …

Although deep reinforcement learning (DRL) algorithms have made important achievements
in many control tasks, they still suffer from the problems of sample inefficiency and unstable …

In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving
complex continuous control tasks like locomotion and dexterous manipulation. However, this …

Model-Based Reinforcement Learning (MBRL) is one category of Reinforcement Learning
(RL) algorithms which can improve sampling efficiency by modeling and approximating …

Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for
complex decision-making tasks. In this paper, we provide an overview of DRL, including its …

Model-free deep reinforcement learning algorithms are able to successfully solve a wide
range of continuous control tasks, but typically require many on-policy samples to achieve …