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Found 9 papers, 3 papers with code
Efficient Multi-Task Reinforcement Learning via Task-Specific Action Correction
Multi-task reinforcement learning (MTRL) demonstrate potential for enhancing the generalization of a robot, enabling it to perform multiple tasks concurrently.
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.
Measuring Policy Distance for Multi-Agent Reinforcement Learning
Furthermore, we extend MAPD to a customizable version, which can quantify differences among agent policies on specified aspects.
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.
Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League
The resulting player vectors discovered 18 different playing styles in the CSL.
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.
