Dec 20, 2018 · In this paper, we extend this framework by introducing multiple primal and dual models, and propose the multi-agent dual learning framework.
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The core idea of dual learning is to leverage the duality between the primal task (mapping from domain X to domain Y) and dual task (mapping from domain Y to X) ...
The core idea of dual learning is to leverage the duality between the primal task (mapping from domain X to domain. Y) and dual task (mapping from domain Y to X) ...
The training objective of the agent is regularized, which is to learn better models by leveraging the relationship between the two agents as feedback,. i.e., ...
Dual learning has attracted much attention in machine learning, computer vision and natural language processing communities. The core idea of dual learning ...
In this paper, a two-tier reinforcement learning model is created to play competitive games and effectively engage in matches with different opponents to ...
Dec 9, 2024 · By incorporating dual Actor-Critic pairs, our framework facilitates effective coordination and learning among multiple agents, leading to ...
Nov 22, 2024 · Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative tasks, demonstrating impressive performance and ...
We propose a new learning paradigm, dual learning, which leverages the primal-dual structure of AI tasks to obtain effective feedback or regularization signals.
Efficiently modeling the nearly decomposable structure and leveraging it to coordinate agents can enhance the learning efficiency of multi-agent reinforcement ...