Decompose a task into generalizable subtasks in multi-agent reinforcement learning
Abstract In recent years, Multi-Agent Reinforcement Learning (MARL) techniques have
made significant strides in achieving high asymptotic performance in single task. However …
made significant strides in achieving high asymptotic performance in single task. However …
Deep reinforcement learning for multi-agent interaction
The development of autonomous agents which can interact with other agents to accomplish
a given task is a core area of research in artificial intelligence and machine learning …
a given task is a core area of research in artificial intelligence and machine learning …
Generating teammates for training robust ad hoc teamwork agents via best-response diversity
A Rahman, E Fosong, I Carlucho… - arXiv preprint arXiv …, 2022 - arxiv.org
Ad hoc teamwork (AHT) is the challenge of designing a robust learner agent that effectively
collaborates with unknown teammates without prior coordination mechanisms. Early …
collaborates with unknown teammates without prior coordination mechanisms. Early …
Learning Top-K Subtask Planning Tree Based on Discriminative Representation Pretraining for Decision-making
J Ruan, K Wang, Q Zhang, D Xing, B Xu - Machine Intelligence Research, 2024 - Springer
Decomposing complex real-world tasks into simpler subtasks and devising a subtask
execution plan is critical for humans to achieve effective decision-making. However …
execution plan is critical for humans to achieve effective decision-making. However …
Learning Embeddings for Sequential Tasks Using Population of Agents
We present an information-theoretic framework to learn fixed-dimensional embeddings for
tasks in reinforcement learning. We leverage the idea that two tasks are similar to each other …
tasks in reinforcement learning. We leverage the idea that two tasks are similar to each other …