8 accepted papers
We introduce FrontierCS, a benchmark of 240 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competitive programming participants and problem setters. Unlike existing benchmarks that focus on tasks with known optimal solut…
Cooperative multi-agent reinforcement learning (MARL) is extensively used for solving complex cooperative tasks, and value decomposition methods are a prevalent approach for this domain. However, these methods have not been successful in addressing both homogeneous and heterogeneous tasks simultaneo…
Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior coordination. Many works propose to abstract teammate instances into high-level representation of types and then pre-train the best response for each type. However, most of them do not consider the distribution of te…
Offline Reinforcement Learning (RL) has demonstrated promising results in various applications by learning policies from previously collected datasets, reducing the need for online exploration and interactions. However, real-world scenarios usually involve partial observability, which brings crucial…
Human brain can effectively integrate visual and auditory information. Dynamic Vision Sensor (DVS) and Dynamic Audio Sensor (DAS) are event-based sensors imitating the mechanism of human retina and cochlea. Since the sensors record the visual and auditory input as asynchronous discrete events, they…
Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of storage and computation. This hinders their deployment on scenarios where resources…
Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (S…
In ad-hoc teamwork, an agent is required to cooperate with unknown teammates without prior coordination. To swiftly adapt to an unknown teammate, most works adopt a type-based approach, which pre-trains the agent with a set of pre-prepared teammate types, then associates the unknown teammate with a…