reproducibilityindex.ai

A Minimalist Approach to Offline Reinforcement Learning

Authors: Scott Fujimoto, Shixiang (Shane) Gu

NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	We evaluate our minimal changes to the TD3 algorithm on the D4RL benchmark of continuous control tasks [Fu et al., 2020]. We ﬁnd that our algorithm compares favorably against many ofﬂine RL algorithms, while being signiﬁcantly easier to implement and more than halving the required computation cost.
Researcher Affiliation	Collaboration	1Mila, Mc Gill University 2Google Research, Brain Team
Pseudocode	No	The paper describes the algorithmic changes using mathematical equations (Equations 1, 2, 3, 4, 5) and textual descriptions, but it does not include a formally labeled 'Pseudocode' or 'Algorithm' block.
Open Source Code	Yes	To accommodate reproduciblity, all of our code is open-sourced1. 1https://github.com/sfujim/TD3_BC
Open Datasets	Yes	We evaluate our proposed approach on the D4RL benchmark of Open AI gym Mu Jo Co tasks [Todorov et al., 2012, Brockman et al., 2016, Fu et al., 2020]
Dataset Splits	Yes	We train each algorithm for 1 million time steps and evaluate every 5000 time steps. Each evaluation consists of 10 episodes.
Hardware Specification	Yes	All run time experiments were run with a single Ge Force GTX 1080 GPU and an Intel Core i7-6700K CPU at 4.00GHz.
Software Dependencies	No	The paper mentions using 'Py Torch [Paszke et al., 2019]' as a framework for re-implementing Fisher-BRC, but it does not specify version numbers for PyTorch or any other software dependencies crucial for reproducibility of the experiments described in the paper.
Experiment Setup	Yes	We train each algorithm for 1 million time steps and evaluate every 5000 time steps. Each evaluation consists of 10 episodes. ... We use α = 2.5 in our experiments. ... Secondly, we normalize the features of every state in the provided dataset.