Graph Neural Networks with Model-based Reinforcement Learning for Multi-agent Systems
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Abstract (arXiv)
Multi-agent systems (MAS) constitute a significant role in exploring machine intelligence and advanced applications. In order to deeply investigate complicated interactions within MAS scenarios, we originally propose "GNN for MBRL" model, which utilizes a state-spaced Graph Neural Networks with Model-based Reinforcement Learning to address specific MAS missions (e.g., Billiard-Avoidance, Autonomous Driving Cars). In detail, we firstly used GNN model to predict future states and trajectories of multiple agents, then applied the Cross-Entropy Method (CEM) optimized Model Predictive Control to assist the ego-agent planning actions and successfully accomplish certain MAS tasks.
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References · 7
- 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
- 2019Model-Based Reinforcement Learning for Ataricited by 495
- 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 99
- 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 96
- 2019Structured Object-Aware Physics Prediction for Video Modeling and Planningcited by 42
- 2020Model-Predictive Control via Cross-Entropy and Gradient-Based Optimizationcited by 32
- 2022Kinova Gemini: Interactive Robot Grasping with Visual Reasoning and Conversational AIcited by 2
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