Reinforcement Learning (RL) is a widely used solution for sequential decision-making problems and has been used in many complex domains. However, RL algorithms suffer from scalability issues, especially when multiple agents are acting in a shared environment. This research intends to accelerate learning in multiagent sequential decision-making tasks by reusing previous knowledge, both from past solutions and advising between agents. We intend to contribute a Transfer Learning framework focused on Multiagent RL, requiring as few domain-specific hand-coded parameters as possible.
CITATION STYLE
Da Silva, F. L., & Costa, A. H. R. (2017). Accelerating multiagent reinforcement learning through transfer learning. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 5034–5035). AAAI press. https://doi.org/10.1609/aaai.v31i1.10518
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