Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL)
algorithms by finding an initial set of parameters for the dynamical model such that the …

Reinforcement learning methods can achieve significant performance but require a large
amount of training data collected on the same robotic platform. A policy trained with …

Although reinforcement learning methods can achieve impressive results in simulation, the
real world presents two major challenges: generating samples is exceedingly expensive …

Learning adaptable policies is crucial for robots to operate autonomously in our complex
and quickly changing world. In this work, we present a new meta-learning method that …

Deep Reinforcement Learning (RL) is mainly studied in a setting where the training and the
testing environments are similar. But in many practical applications, these environments may …

We introduce PACOH-RL, a novel model-based Meta-Reinforcement Learning (Meta-RL)
algorithm designed to efficiently adapt control policies to changing dynamics. PACOH-RL …

Humans and animals can learn complex predictive models that allow them to accurately and
reliably reason about real-world phenomena, and they can adapt such models extremely …

Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to
quickly acquire new behaviors by leveraging prior experience in a set of related training …

Modern reinforcement learning methods suffer from low sample efficiency and unsafe
exploration, making it infeasible to train robotic policies entirely on real hardware. In this …

Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they
are trained exclusively for specific objectives and require massive amounts of interaction to …