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Found 7 papers, 3 papers with code
Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
Furthermore, we design a prioritized policy gradient approach to compensate for the gap caused by differences in the number of different types of agents.
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph
This paper proposes a novel hierarchical MARL model called Hierarchical Cooperation Graph Learning (HCGL) for solving general multi-agent problems.
Learning Heterogeneous Agent Cooperation via Multiagent League Training
However, they also come with challenges compared with homogeneous systems for multiagent reinforcement learning, such as the non-stationary problem and the policy version iteration issue.
A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning
SOTA multiagent reinforcement algorithms distinguish themselves in many ways from their single-agent equivalences.
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning
Next, based on this novel graph structure, we propose a Cooperation Graph Multiagent Reinforcement Learning (CG-MARL) algorithm, which can efficiently deal with the sparse reward problem in multiagent tasks.
Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems
Second, distinct from the well-known attention mechanism, ConcNet has a unique motivational subnetwork to explicitly consider the motivational indices when scoring the observed entities.
Learning an Adaptive Model for Extreme Low-light Raw Image Processing
Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene.
