With the recent development of deep learning technology comes the wide use of artificial
intelligence (AI) models in various domains. AI shows good performance for definite …

A key challenge for reinforcement learning (RL) consists of learning in environments with
sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new …

Various automatic curriculum learning (ACL) methods have been proposed to improve the
sample efficiency and final performance of robots' policies learning. They are designed to …

In real-world environments, autonomous agents rely on their egocentric observations. They
must learn adaptive strategies to interact with others who possess mixed motivations …

Lightweight child-worn recorders that collect audio across an entire day allow for a big-data
approach to the study of language development. By collecting the child's production and …

Heterogeneous Multi-unit control is one of the most concerned topic in multi-agent system,
which focuses on controlling agents of different type of functions. Methods that utilize …

Artificial Intelligence algorithms never operate in isolation but are always part of broader
processes that often involve humans, other computer algorithms, incentive structures, and …

**化学**算法依赖于精心设计的外在奖励, 然而Agent 在和环境交互过程中, 环境反馈给Agent
的外在奖励往往是非常稀少的或延迟, 这导致了Agent 无法学**到一个好的策略 …

Reinforcement learning is a powerful paradigm for training agents to acquire complex
behaviors, but it assumes that an external reward is provided by the environment. In …

● Water demand has to be delivered● Storage tanks must not overflow or run out of
water● A minimum water reserve has to be in the tanks● A minimum pressure must be …