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Found 20 papers, 1 papers with code
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning
The entire framework has a message module and an action module.
Adaptive parameter sharing for multi-agent reinforcement learning
Parameter sharing, as an important technique in multi-agent systems, can effectively solve the scalability issue in large-scale agent problems.
Mastering Complex Coordination through Attention-based Dynamic Graph
The coordination between agents in multi-agent systems has become a popular topic in many fields.
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS).
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems
Our research contributes to the development of an effective and adaptable asynchronous action coordination method that can be widely applied to various task types and environmental configurations in MAS.
From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL
Centralized training with decentralized execution (CTDE) is a widely-used learning paradigm that has achieved significant success in complex tasks.
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning
In addition, our structure can be applied to various existing mainstream reinforcement learning algorithms with minor modifications and can deal with the problem with a variable number of agents.
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish equilibrium and achieve coordination depending on game structure.
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning
In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable.
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning
Value decomposition methods have gained popularity in the field of cooperative multi-agent reinforcement learning.
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning
Almost all multi-agent reinforcement learning algorithms without communication follow the principle of centralized training with decentralized execution.
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network
The application of deep reinforcement learning in multi-agent systems introduces extra challenges.
Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution
HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nodes and hyperedges among nodes indicate that the corresponding agents can coordinate to achieve larger rewards.
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism
Recently, some challenging tasks in multi-agent systems have been solved by some hierarchical reinforcement learning methods.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+4
The $f$-Divergence Reinforcement Learning Framework
In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards.
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning
In the real world, many tasks require multiple agents to cooperate with each other under the condition of local observations.
Distributional Reinforcement Learning
reinforcement-learning
+3
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning
As one of the solutions to the decentralized partially observable Markov decision process (Dec-POMDP) problems, the value decomposition method has achieved significant results recently.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition
In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.
Ranked #54 on
Skeleton Based Action Recognition
on NTU RGB+D
A generalized parametric 3D shape representation for articulated pose estimation
We present a novel parametric 3D shape representation, Generalized sum of Gaussians (G-SoG), which is particularly suitable for pose estimation of articulated objects.
