With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

We approach the problem of learning by watching humans in the wild. While traditional
approaches in Imitation and Reinforcement Learning are promising for learning in the real …

CORL is an open-source library that provides thoroughly benchmarked single-file
implementations of both deep offline and offline-to-online reinforcement learning algorithms …

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

To build general robotic agents that can operate in many environments, it is often imperative
for the robot to collect experience in the real world. However, this is often not feasible due to …

In this paper, we present Tianshou, a highly modularized Python library for deep
reinforcement learning (DRL) that uses PyTorch as its backend. Tianshou intends to be …

Recently, methods such as Decision Transformer that reduce reinforcement learning to a
prediction task and solve it via supervised learning (RvS) have become popular due to their …

Advances in tunnel boring machines (TBM) have leveraged applied artificial intelligence to
promote sustainable and automatic tunneling construction. This paper highlights the …

Offline reinforcement learning methods hold the promise of learning policies from pre-
collected datasets without the need to query the environment for new samples. This setting …