Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems,
trains an agent to achieve different goals under particular scenarios. Compared to the …

Diffusion models surpass previous generative models in sample quality and training
stability. Recent works have shown the advantages of diffusion models in improving …

One important aspect of effective human--robot collaborations is the ability for robots to
adapt quickly to the needs of humans. While techniques like deep reinforcement learning …

Pre-training on task-agnostic large datasets is a promising approach for enhancing the
sample efficiency of reinforcement learning (RL) in solving complex tasks. We present …

Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally
challenging. Existing approaches have utilized goal relabeling on collected experiences to …

Recently, many researchers have made successful progress in building the AI systems for
MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of …

Solving reinforcement learning (RL)-based mapless navigation tasks is challenging due to
their sparse reward and long decision horizon nature. Hierarchical reinforcement learning …

Sample efficiency remains a key challenge for the deployment of deep reinforcement
learning (RL) in real-world scenarios. A common approach is to learn efficient …

High-dimension inputs limit the sample efficiency of deep reinforcement learning, increasing
the difficulty of applying it to real-world continuous control tasks, especially in uncertain …