Data-driven model predictive control has two key advantages over model-free methods: a
potential for improved sample efficiency through model learning, and better performance as …

During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents
perform a significant number of random exploratory steps. In the real world, this can limit the …

AI systems empowered by reinforcement learning (RL) algorithms harbor the immense
potential to catalyze societal advancement, yet their deployment is often impeded by …

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

Controlling artificial agents from visual sensory data is an arduous task. Reinforcement
learning (RL) algorithms can succeed but require large amounts of interactions between the …

The widespread application of Reinforcement Learning (RL) in real-world situations is yet to
come to fruition, largely as a result of its failure to satisfy the essential safety demands of …

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

The goal of reinforcement learning (RL) is to find a policy that maximizes the expected
cumulative return. It has been shown that this objective can be represented as an …

The objective of lifelong reinforcement learning (RL) is to optimize agents which can
continuously adapt and interact in changing environments. However, current RL approaches …

A key component of model-based reinforcement learning (RL) is a dynamics model that
predicts the outcomes of actions. Errors in this predictive model can degrade the …