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Found 21 papers, 0 papers with code
KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph
Large language model (LLM) has achieved outstanding performance on various downstream tasks with its powerful natural language understanding and zero-shot capability, but LLM still suffers from knowledge limitation.
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.
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.
An Information-Theoretic Analysis of Discrete-Time Control and Filtering Limitations by the I-MMSE Relationships
Fundamental limitations or performance trade-offs/limits are important properties and constraints of control and filtering systems.
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
Simplified Analysis on Filtering Sensitivity Trade-offs in Continuous- and Discrete-Time Systems
A simplified analysis is performed on the Bode-type filtering sensitivity trade-off integrals, which capture the sensitivity characteristics of the estimate and estimation error with respect to the process input and estimated signal in continuous- and discrete-time linear time-invariant filtering systems.
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network
The application of deep reinforcement learning in multi-agent systems introduces extra challenges.
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
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
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions
Our algorithm not only achieves near-optimal regret in the stochastic setting, but also obtains a regret with an additive term of corruption in the corrupted setting, while maintaining efficient communication.
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
Squeezed-field path-integral description of BCS superconductors
We develop a squeezed-field path-integral representation for BCS superconductors utilizing a generalized completeness relation of squeezed-fermionic coherent states.
Superconductivity
f-Divergence Variational Inference
This paper introduces the $f$-divergence variational inference ($f$-VI) that generalizes variational inference to all $f$-divergences.
OSU Multimodal Machine Translation System Report
This paper describes Oregon State University's submissions to the shared WMT'17 task "multimodal translation task I".
