Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
Authors: Felipe Leno Da Silva, Anna Helena Reali Costa
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems Felipe Leno Da Silva EMAIL Anna Helena Reali Costa EMAIL Computer Engineering Department Universidade de São Paulo São Paulo, SP, Brazil. Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions. |
| Researcher Affiliation | Academia | Felipe Leno Da Silva EMAIL Anna Helena Reali Costa EMAIL Computer Engineering Department Universidade de São Paulo São Paulo, SP, Brazil. |
| Pseudocode | No | The paper is a survey and describes various algorithms using mathematical formulations and descriptive text, but it does not present any structured pseudocode blocks or algorithms with explicit labels like 'Algorithm 1'. |
| Open Source Code | No | The paper does not provide open-source code for the methodology it describes. Section 8, titled 'Resources', mentions existing third-party libraries like BURLAP, RL-Glue, and Open AI Gym, but these are tools used by the community, not code released by the authors for the specific work presented in this survey paper. |
| Open Datasets | No | The paper is a survey of existing literature and does not present its own experimental results based on specific datasets. While it discusses experimental domains (Section 6), it does not provide access information for any datasets used in its own (non-existent) experiments. |
| Dataset Splits | No | The paper is a survey and does not present new experimental results; therefore, it does not provide information on training/test/validation dataset splits. |
| Hardware Specification | No | The paper is a survey and does not present new experimental results; therefore, it does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper is a survey of existing literature and does not present its own experimental results; therefore, it does not list specific software dependencies with version numbers for its own methodology. It mentions general libraries like BURLAP, RL-Glue, and Open AI Gym in Section 8 'Resources', but these are not dependencies for the paper's own work. |
| Experiment Setup | No | The paper is a survey and does not present new experimental results; therefore, it does not provide specific experimental setup details, hyperparameters, or system-level training settings. |