Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning
Authors: Weijian Liao, Zongzhang Zhang, Yang Yu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 |