Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …

An accurate model of the environment and the dynamic agents acting in it offers great
potential for improving motion planning. We present MILE: a Model-based Imitation …

Abstract Model-based reinforcement learning (RL) algorithms, which learn a dynamics
model from logged experience and perform conservative planning under the learned model …

Offline reinforcement learning (RL) defines the task of learning from a static logged dataset
without continually interacting with the environment. The distribution shift between the …

In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL)
library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy …

Offline Reinforcement Learning (RL) aims to learn policies from previously collected
datasets without exploring the environment. Directly applying off-policy algorithms to offline …

In offline reinforcement learning, weighted regression is a common method to ensure the
learned policy stays close to the behavior policy and to prevent selecting out-of-sample …

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 …

Offline reinforcement learning (RL) can learn control policies from static datasets but, like
standard RL methods, it requires reward annotations for every transition. In many cases …

Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-
based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution …