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Found 3 papers, 1 papers with code
Value-Decomposition Multi-Agent Actor-Critics
To obtain a reasonable trade-off between training efficiency and algorithm performance, we extend value-decomposition to actor-critics that are compatible with A2C and propose a novel actor-critic framework, value-decomposition actor-critics (VDACs).
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+2
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication
The flexibility of the graph structure enables our method to be applicable to a variety of multi-agent systems, e. g. dynamic systems that consist of varying numbers of agents and static systems with a fixed number of agents.
Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration
The ability to model and predict ego-vehicle's surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems.
