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VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of ...
Multi-agent reinforcement learning is the study of numerous artificial intelligence agents cohabitating in an environment, often collaborating toward some end goal. It deals with having only one actor ...
In this paper we present techniques for centralized training of Multi-Agent (Deep) Reinforcement Learning (MARL) using the model-free Deep Q-Network as the baseline model and message sharing between ...
We evaluate LGTC-IPPO against standard multi-agent reinforcement learning baselines and a centralized expert solution across a range of team sizes and resource distributions. Experimental results ...
In this paper, we propose the IFresher framework to improve the timeliness of multi-agent mobile augmented reality (MAR) systems. Existing works have made strides in accuracy-latency trade-offs, but ...
“Multi-agent reinforcement learning has been picking up traction in the research community over the last couple of years, and what we need right now is a series of ambitious tasks that the community ...
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of ...
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