A Multiagent Framework for the Asynchronous and Collaborative Extension of Multitask ML Systems
The traditional ML development methodology does not enable a large number of contributors, each with distinct objectives, to work collectively on the creation and extension of a shared intelligent system. Enabling such a collaborative methodology can accelerate the rate of innovation, increase ML technologies accessibility and enable the emergence of novel capabilities. We believe that this novel methodology for ML development can be demonstrated through a modularized representation of ML models and the definition of novel abstractions allowing to implement and execute diverse methods for the asynchronous use and extension of modular intelligent systems. We present a multiagent framework for the collaborative and asynchronous extension of dynamic large-scale multitask systems.
Code (1)
Similar Papers 제목 키워드 기반
Conditional Max-Sum for Asynchronous Multiagent Decision Making
In this paper we present a novel approach for multiagent decision making in dynamic environments based on Factor Graphs and the Max-Sum algorithm, considering asynchronous variable reassignments and distributed message-p…
Decision MakingMultiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset
Large-scale datasets have fueled recent advancements in AI-based autonomous vehicle research. However, these datasets are usually collected from a single vehicle's one-time pass of a certain location, lacking multiagent …
3D ReconstructionAutonomous VehiclesObject DiscoveryMultiagent Reinforcement Learning with Neighbor Action Estimation
Multiagent reinforcement learning, as a prominent intelligent paradigm, enables collaborative decision-making within complex systems. However, existing approaches often rely on explicit action exchange between agents to …
Reinforcement LearningMultiagent Cooperation and Competition with Deep Reinforcement Learning
Multiagent systems appear in most social, economical, and political situations. In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigat…
Deep Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+1COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS
A deep reinforcement learning solution is developed for a collaborative multiagent system. Individual agents choose actions in response to the state of the environment, their own state, and possibly partial information a…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)