Dec 14, 2023 · We propose a novel parameter sharing method. It maps each type of agent to different regions within a shared network based on their identity.
Parameter sharing, as an important technique in multi-agent systems, can effectively solve the scalability issue in large- scale agent problems.
Dec 14, 2023 · Parameter sharing ensures that the model size remains unchanged as the number of agents changes by sharing the same parameterized function among ...
Parameter sharing, as an important technique in multi-agent systems, can effectively solve the scalability issue in large-scale agent problems.
Apr 28, 2024 · In ADMN, modules are shared among agents to improve the training efficiency, while the combination of different modules brings rich diversity.
Aug 24, 2024 · We design self-supervised learning tasks to extract the implicit behavioral characteristics from the action trajectories of agents. Based on the ...
Missing: Adaptive | Show results with:Adaptive
Furthermore, as parameter sharing is the most centralized form of learning, we show it bypasses nonstationarity in multi-agent reinforcement learning. We ...
Sep 19, 2024 · Parameter sharing, on the other hand, is used for homogeneous agents, where a single neural network governs all agent behaviors. Experience ...
In multi-agent reinforcement learning (MARL), parameter sharing is commonly employed to enhance sample efficiency. However, the popular approach of full.
To efficiently learn multiple downstream tasks we introduce Task Adaptive Parameter Sharing (TAPS), a general method for tuning a base model to a new task by ...
Missing: agent reinforcement