9 accepted papers
Offline goal-conditioned reinforcement learning remains challenging for long-horizon tasks. While hierarchical approaches mitigate this issue by decomposing tasks, most existing methods rely on separate high- and low-level networks and generate only a single intermediate subgoal, making them inadequ…
End-to-end autonomous driving systems have demonstrated advantages over traditional modular systems. Despite this progress, these end-to-end systems still struggle to be deployed in real-world driving environments, as they inevitably encounter undertrained scenarios in which autonomous vehicles may
Reinforcement learning (RL) typically assumes repetitive resets to provide an agent with diverse and unbiased experiences. These resets require significant human intervention and result in poor training efficiency in real-world settings. Autonomous RL (ARL) addresses this challenge by jointly traini…
A significant bottleneck in applying current reinforcement learning algorithms to real-world scenarios is the need to reset the environment between every episode. This reset process demands substantial human intervention, making it difficult for the agent to learn continuously and autonomously. Seve
One of the unresolved challenges for autonomous vehicles is safe navigation among occluded pedestrians and vehicles. Previous approaches included generating phantom vehicles and assessing their risk, but they often made the ego vehicle overly conservative or could not conduct a real-time risk assess
Learning shared structures across changing environments enables an agent to efficiently retain obtained knowledge and transfer it between environments. A skill is a promising concept to represent shared structures. Several recent works proposed unsupervised skill discovery algorithms that can discov…
How to explore environments is one of the most critical factors for the performance of an agent in reinforcement learning. Conventional exploration strategies such as ε-greedy algorithm and Gaussian exploration noise simply depend on pure randomness. However, it is required for an agent to consider…
Most real-world tasks are compound tasks that consist of multiple simpler sub-tasks. The main challenge of learning compound tasks is that we have no explicit supervision to learn the hierarchical structure of compound tasks. To address this challenge, previous imitation learning methods exploit tas…
Over the last decade, automated vehicles have been widely researched and their massive potential has been verified through several milestone demonstrations. However, there are still many challenges ahead. One of the biggest challenges is integrating them into urban environments in which dilemmas occ…