Goal-conditioned Reinforcement Learning (RL) holds promise for addressing intricate
control challenges by enabling agents to learn and execute desired skills through separate …

Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …

Reinforcement Learning (RL) over motion skill space has been verified to generate more
diverse behaviors than that over low-level control space, and has exhibited superior …

Efficient and effective exploration in continuous space is a central problem in applying
reinforcement learning (RL) to autonomous driving. Skills learned from expert …

Reinforcement learning (RL) requires skillful definition and remarkable computational efforts
to solve optimization and control problems, which could impair its prospect. Introducing …

Reinforcement learning (RL) is an effective approach to motion planning in autonomous
driving, where an optimal driving policy can be automatically learned using the interaction …

Reinforcement learning has emerged as an important approach for autonomous driving. A
reward function is used in reinforcement learning to establish the learned skill objectives …

The thriving artificial intelligence (AI) technologies have been used to address various
challenges in the physical world. Currently, AI methods are widely used in perception …

Autonomous vehicle racing has attracted extensive interest due to its great potential in
autonomous driving at the extreme limits. Model-based and learning-based methods are …

Reinforcement learning (RL) has shown promising performance in autonomous driving
applications in recent years. The early end-to-end RL method is usually unexplainable and …