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Found 8 papers, 1 papers with code
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.
D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network
Towards this problem, a recent method, \textit{Deep CFR} alleviates the need for abstraction and expert knowledge by applying deep neural networks directly to CFR in full games.
Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval
In contrast to previous works, we focus on the reproducibility of the approaches and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text.
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning
However, due to the slow sample collection and poor sample exploration, there are still some problems in multi-agent reinforcement learning, such as unstable model iteration and low training efficiency.
Parameter-Efficient and Student-Friendly Knowledge Distillation
In this paper, we present a parameter-efficient and student-friendly knowledge distillation method, namely PESF-KD, to achieve efficient and sufficient knowledge transfer by updating relatively few partial parameters.
Dynamic Contrastive Distillation for Image-Text Retrieval
Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major drawback: the ever-increasing size of VLP models restricts its deployment to real-world search scenarios (where the high latency is unacceptable).
An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning
To enable the pre-trained models to be fine-tuned with local data on edge devices without sharing data with the cloud, we design an efficient split fine-tuning (SFT) framework for edge and cloud collaborative learning.
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning
In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
