Dec 27, 2022 · In this study, we introduce a new exploration method with the strangeness that can be easily incorporated into any centralized training and ...
Our method suggests that the degree of strangeness increases when the observations are new or when the agent's path of observation is not aligned with its ...
This repository refers to open source PyMARL and SMAC, and is created for the purpose of experimental research on exploration method in MARL.
Jun 25, 2024 · The method uses the concept of strangeness, which is determined by evaluating (1) the level of the unfamiliarity of the observations an agent ...
In this study, we introduce a new exploration method with the strangeness that can be easily incorporated into any centralized training and decentralized ...
It uses revelation, reinforcement, reflection and re-examination to explicitly explore emerging themes in interpretive case study research. The method is based ...
The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks.
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Jun 10, 2024 · In this paper, we introduce a novel Episodic Multi-agent reinforcement learning with Curiosity-driven exploration, called EMC. We leverage an ...
Strangeness-driven exploration in multi-agent reinforcement learning. JB Kim, HB Choi, YH Han. Neural Networks 172, 106149, 2024. 2, 2024. System and method for ...
Efficient exploration strategy is one of essential issues in cooperative multi-agent reinforcement learning (MARL) algorithms requiring complex coordination. 2.