Analysis of Stochastic Processes through Replay Buffers
Authors: Shirli Di-Castro, Shie Mannor, Dotan Di Castro
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we analyze a system where a stochastic process X is pushed into a replay buffer and then randomly sampled to generate a stochastic process Y from the replay buffer. We provide an analysis of the properties of the sampled process such as stationarity, Markovity and autocorrelation in terms of the properties of the original process. Our theoretical analysis sheds light on why replay buffer may be a good de-correlator. Our analysis provides theoretical tools for proving the convergence of replay buffer based algorithms which are prevalent in reinforcement learning schemes. |
| Researcher Affiliation | Collaboration | 1Technion Institute of Technology, Haifa, Israel 2NVIDIA Research, Israel 3Bosch Center of AI, Haifa, Israel. |
| Pseudocode | Yes | Algorithm 1 Linear Actor Critic with RB samples |
| Open Source Code | No | The paper does not provide any links to open-source code or explicit statements about its release. |
| Open Datasets | No | The paper does not describe experiments using a specific dataset nor does it provide information about the public availability of any dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental data splits (training, validation, test) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers required for reproducibility. |
| Experiment Setup | No | The paper presents a theoretical analysis and an algorithm but does not provide specific experimental setup details such as hyperparameters or training configurations. |