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 [1].
Robust Imitation of Diverse Behaviors
Authors: Ziyu Wang, Josh S. Merel, Scott E. Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our approach on learning diverse gaits from demonstration on a 2D biped and a 62 Do F 3D humanoid in the Mu Jo Co physics environment. |
| Researcher Affiliation | Industry | Deep Mind ziyu,jsmerel,reedscot,gregwayne,nandodefreitas,EMAIL |
| Pseudocode | Yes | Algorithm 1 Diverse generative adversarial imitation learning. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code for the methodology described. |
| Open Datasets | Yes | The training set consists of 250 random trajectories from 6 different neural network controllers that were trained to match 6 different movement styles from the CMU motion capture data base4. |
| Dataset Splits | No | The paper describes training and test data generation/usage, but does not explicitly mention a distinct validation set or specific splits for it. |
| Hardware Specification | No | The paper mentions the use of the Mu Jo Co physics engine for simulations but does not specify any hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper mentions software components like MuJoCo, LSTM, WaveNet, and TRPO, but does not specify their version numbers or other detailed software dependencies required for replication. |
| Experiment Setup | Yes | For details of the simulation and the experimental setup please see appendix. |