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 …

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

In autonomous driving predicting future events in advance and evaluating the foreseeable
risks empowers autonomous vehicles to plan their actions enhancing safety and efficiency …

Settling the sample complexity of model-based offline reinforcement learning Page 1 The
Annals of Statistics 2024, Vol. 52, No. 1, 233–260 https://doi.org/10.1214/23-AOS2342 © …

Connected and automated vehicles (CAVs) require multiple tasks in their seamless
maneuverings. Some essential tasks that require simultaneous management and actions …

This paper proposes a two-stage trajectory planning method for self-driving vehicles (SDVs)
in intersection scenarios with uncertain social circumstances while considering other traffic …

The learned policy of model-free offline reinforcement learning (RL) methods is often
constrained to stay within the support of datasets to avoid possible dangerous out-of …

Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications such …

In the rapidly evolving landscape of autonomous driving, the capability to accurately predict
future events and assess their implications is paramount for both safety and efficiency …

Learning-based vehicle planning is receiving increasing attention with the emergence of
diverse driving simulators and large-scale driving datasets. While offline reinforcement …