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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning
Authors: Weijian Liao, Zongzhang Zhang, Yang Yu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on Deep Mind control tasks with default and distracting backgrounds. By statistically reliable evaluation protocols, our experiments demonstrate our approach is superior to previous metric-based methods in terms of sample efficiency and asymptotic performance in both backgrounds. |
| Researcher Affiliation | Academia | 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China 2 Peng Cheng Laboratory, Shenzhen, 518055, China |
| Pseudocode | No | The paper does not contain any clearly labeled |
| Open Source Code | No | The paper states: |
| Open Datasets | Yes | We conduct experiments on the Deep Mind Control Suite (Tassa et al. 2018)... we randomly sample multiple videos from the kinetics dataset (Kay et al. 2017) as background... |
| Dataset Splits | No | The paper describes evaluation protocols (e.g., IQM normalized score) and the number of runs (10 seeds for 8 tasks) but does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper states: |
| Software Dependencies | No | The paper refers to various algorithms and frameworks (e.g., SAC, Q-learning, DDPG, TD3) but does not provide specific version numbers for any software dependencies like programming languages or libraries (e.g., Python version, TensorFlow/PyTorch version). |
| Experiment Setup | No | The paper mentions that |