Transfer Learning for Multiagent Reinforcement Learning Systems
Authors: Felipe Leno da Silva, Anna Helena Reali Costa
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This research aims to propose a Transfer Learning (TL) framework to accelerate learning by exploiting two knowledge sources: (i) previously learned tasks; and (ii) advising from a more experienced agent. The definition of such framework requires answering several challenging research questions... 4 Partial Results In order to define a representation which allows knowledge generalization, we propose an OO-MDP extension to MAS, called Multiagent Object-Oriented MDP (MOO-MDP). This extension if fully described on an article submitted to ECAI 2016 Main Track, in which an algorithm to solve deterministic cooperative MOO-MDPs is also presented. 5 Next Steps MOO-MDP is a promising model which allows knowledge generalization. Now, the next step in our research is to define how to transfer learned knowledge through tasks or agents. |
| Researcher Affiliation | Academia | Felipe Leno da Silva and Anna Helena Reali Costa Escola Polit ecnica da Universidade de S ao Paulo, S ao Paulo, Brazil {f.leno,anna.reali}@usp.br |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | No | This paper introduces a theoretical framework and a model, and does not mention using any datasets or providing information about their public availability. |
| Dataset Splits | No | The paper focuses on a theoretical framework and does not describe any experimental setup involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not describe any experiments or specify hardware used for running them. |
| Software Dependencies | No | The paper does not describe any experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes a theoretical framework and does not include specific experimental setup details such as hyperparameter values or training configurations. |