Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

In this review, the industry's current issues regarding intelligent manufacture are presented.
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …

Exploration is critical for good results in deep reinforcement learning and has attracted much
attention. However, existing multi-agent deep reinforcement learning algorithms still use …

Randomized ensembled double Q-learning (REDQ)(Chen et al., 2021b) has recently
achieved state-of-the-art sample efficiency on continuous-action reinforcement learning …

Generalizing policies across different domains with dynamics mismatch poses a significant
challenge in reinforcement learning. For example, a robot learns the policy in a simulator …

Abstract Model-based reinforcement learning (RL) often achieves higher sample efficiency
in practice than model-free RL by learning a dynamics model to generate samples for policy …

This paper discusses model estimation in offline model-based reinforcement learning
(MBRL), which is important for subsequent policy improvement using an estimated model …

Dyna-style model-based reinforcement learning contains two phases: model rollouts to
generate sample for policy learning and real environment exploration using current policy …

Compared to model-free reinforcement learning (RL), model-based RL is often more sample
efficient by leveraging a learned dynamics model to help decision making. However, the …

Abstract Model-based reinforcement learning has attracted wide attention due to its superior
sample efficiency. Despite its impressive success so far, it is still unclear how to …