Provably efficient multi-task reinforcement learning with model transfer
Authors: Chicheng Zhang, Zhi Wang
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player information sharing. We design and analyze an algorithm based on the idea of model transfer, and provide gap-dependent and gap-independent upper and lower bounds that characterize the intrinsic complexity of the problem. |
| Researcher Affiliation | Academia | Chicheng Zhang University of Arizona EMAIL Zhi Wang University of California San Diego EMAIL |
| Pseudocode | Yes | Algorithm 1: MULTI-TASK-EULER |
| Open Source Code | No | No explicit statement or link for open-source code for the described methodology was found. |
| Open Datasets | No | As a theoretical paper, no specific dataset is used for training or evaluation. The paper describes a problem setting in tabular episodic Markov decision processes (MDPs) rather than using a concrete dataset. |
| Dataset Splits | No | As a theoretical paper, there is no mention of train/validation/test dataset splits. |
| Hardware Specification | No | As a theoretical paper, no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | As a theoretical paper, no specific software dependencies with version numbers for experimental replication are mentioned. |
| Experiment Setup | No | As a theoretical paper, no specific experimental setup details such as hyperparameters or training configurations are provided. |