CESMA: Centralized Expert Supervises Multi-Agents
Lin et al.: https://arxiv.org/abs/1902.02311
#MultiagentSystems #ArtificialIntelligence #MachineLearning #Systems #Control
🔗 CESMA: Centralized Expert Supervises Multi-Agents
We consider the reinforcement learning problem of training multiple agents in order to maximize a shared reward. In this multi-agent system, each agent seeks to maximize the reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to other agent policies and thus each agent observes a non-stationary and partially-observable environment. In order to resolve this issue, we demonstrate a novel multi-agent training framework that first turns a multi-agent problem into a single-agent problem to obtain a centralized expert that is then used to guide supervised learning for multiple independent agents with the goal of decentralizing the policy. We additionally demonstrate a way to turn the exponential growth in the joint action space into a linear growth for the centralized policy. Overall, the problem is twofold: the problem of obtaining a centralized expert, and then the problem of supervised learning to train the multi-agents. We demonstra
Lin et al.: https://arxiv.org/abs/1902.02311
#MultiagentSystems #ArtificialIntelligence #MachineLearning #Systems #Control
🔗 CESMA: Centralized Expert Supervises Multi-Agents
We consider the reinforcement learning problem of training multiple agents in order to maximize a shared reward. In this multi-agent system, each agent seeks to maximize the reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to other agent policies and thus each agent observes a non-stationary and partially-observable environment. In order to resolve this issue, we demonstrate a novel multi-agent training framework that first turns a multi-agent problem into a single-agent problem to obtain a centralized expert that is then used to guide supervised learning for multiple independent agents with the goal of decentralizing the policy. We additionally demonstrate a way to turn the exponential growth in the joint action space into a linear growth for the centralized policy. Overall, the problem is twofold: the problem of obtaining a centralized expert, and then the problem of supervised learning to train the multi-agents. We demonstra
CESMA: Centralized Expert Supervises Multi-Agents
Lin et al.: https://arxiv.org/abs/1902.02311
#MultiagentSystems #ArtificialIntelligence #MachineLearning #Systems #Control
🔗 CESMA: Centralized Expert Supervises Multi-Agents
We consider the reinforcement learning problem of training multiple agents in order to maximize a shared reward. In this multi-agent system, each agent seeks to maximize the reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to other agent policies and thus each agent observes a non-stationary and partially-observable environment. In order to resolve this issue, we demonstrate a novel multi-agent training framework that first turns a multi-agent problem into a single-agent problem to obtain a centralized expert that is then used to guide supervised learning for multiple independent agents with the goal of decentralizing the policy. We additionally demonstrate a way to turn the exponential growth in the joint action space into a linear growth for the centralized policy. Overall, the problem is twofold: the problem of obtaining a centralized expert, and then the problem of supervised learning to train the multi-agents. We demonstra
Lin et al.: https://arxiv.org/abs/1902.02311
#MultiagentSystems #ArtificialIntelligence #MachineLearning #Systems #Control
🔗 CESMA: Centralized Expert Supervises Multi-Agents
We consider the reinforcement learning problem of training multiple agents in order to maximize a shared reward. In this multi-agent system, each agent seeks to maximize the reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to other agent policies and thus each agent observes a non-stationary and partially-observable environment. In order to resolve this issue, we demonstrate a novel multi-agent training framework that first turns a multi-agent problem into a single-agent problem to obtain a centralized expert that is then used to guide supervised learning for multiple independent agents with the goal of decentralizing the policy. We additionally demonstrate a way to turn the exponential growth in the joint action space into a linear growth for the centralized policy. Overall, the problem is twofold: the problem of obtaining a centralized expert, and then the problem of supervised learning to train the multi-agents. We demonstra
📃 Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
Wang et al.: https://arxiv.org/abs/2006.13164
#GraphNeuralNetworks #MultiagentSystems #Robotics
Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
Wang et al.: https://arxiv.org/abs/2006.13164
#GraphNeuralNetworks #MultiagentSystems #Robotics
VK
Data Science / Machine Learning / AI / Big Data
Joint Object Detection and Multi-Object Tracking with Graph Neural Networks Wang et al.: https://arxiv.org/abs/2006.13164 #GraphNeuralNetworks #MultiagentSystems #Robotics
Data Science / Machine Learning / AI / Big Data (VK)
Continuous Coordination As a Realistic Scenario for Lifelong Learning
Nekoei et al.: https://arxiv.org/abs/2103.03216
#MachineLearning #ArtificialIntelligence #MultiagentSystems
Continuous Coordination As a Realistic Scenario for Lifelong Learning
Nekoei et al.: https://arxiv.org/abs/2103.03216
#MachineLearning #ArtificialIntelligence #MultiagentSystems
Data Science / Machine Learning / AI / Big Data (VK)
baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents
Michael A. Alcorn, Anh Nguyen: https://arxiv.org/abs/2104.11980
#ArtificialIntelligence #MachineLearning #MultiagentSystems
baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents
Michael A. Alcorn, Anh Nguyen: https://arxiv.org/abs/2104.11980
#ArtificialIntelligence #MachineLearning #MultiagentSystems