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].
Contextual Covariance Matrix Adaptation Evolutionary Strategies
Authors: Abbas Abdolmaleki, Bob Price, Nuno Lau, Luis Paulo Reis, Gerhard Neumann
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For illustration of the algorithm, we will use contextual standard functions and two contextual simulated robotic tasks which are robot table tennis, and a robot kick task. We show that our contextual CMA-ES algorithm performs favourably in comparison to other contextual learning algorithms. |
| Researcher Affiliation | Collaboration | Abbas Abdolmaleki1,2,3, Bob Price5, Nuno Lau1, Luis Paulo Reis2,3, Gerhard Neumann4 1: IEETA, DETI, University of Aveiro 2: DSI, University of Minho 3: LIACC, University of Porto, Portugal 4: CLAS, TU Darmstadt, Germany 5: PARC, A Xerox Company, USA |
| Pseudocode | Yes | Algorithm 1 Contextual Stochastic Search Algorithms |
| Open Source Code | Yes | Matlab code for reproducing the results on standard functions as well as videos regarding the experiments (table tennis and robot kick) at https://goo.gl/MLz Ks W |
| Open Datasets | Yes | We chose two series of optimization tasks for comparisons. In the ο¬rst series, we use the standard optimization test functions [Molga and Smutnicki, 2005] |
| Dataset Splits | No | No explicit training/validation/test dataset splits (percentages, counts, or references to predefined splits) were found. The paper describes an iterative optimization process based on generated samples rather than fixed data partitions. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments were mentioned. |
| Software Dependencies | No | For example we use fmincon tool in matlab. |
| Experiment Setup | Yes | For all experiments, the KL-bound Ο΅ for REPS is set to 1 and for all experiments we use the default hyper parameter settings given in Algorithm 1 without further tuning. |