A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
Authors: Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings. |
| Researcher Affiliation | Academia | 1Royal Holloway, University of London, UK 2King Saud University, Saudi Arabia {pallavi.bagga, nicola.paoletti}@rhul.ac.uk, balrayes@ksu.edu.sa, kostas.stathis@rhul.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks clearly labeled as such. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it include an explicit statement about the release of source code for the methodology described. |
| Open Datasets | No | The paper states, "In order to collect the dataset to train the ANEGMA agent using an SL model, we have used a simulation environment [Alrayes et al., 2016] that supports concurrent negotiations between buyers and sellers." However, it does not provide concrete access information (link, DOI, repository name, or formal citation) for the generated dataset itself to make it publicly available. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, and testing needed to reproduce the data partitioning. While it mentions "test data from 101 simulations", it doesn't specify how the overall dataset was split for training and validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms and models like DDPG and ANN, but it does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | Section 5.1, "Design of the Experiments", details the seller strategies, simulation parameters (MD, MR, tend, Zo A) with qualitative and quantitative values (Table 2), and states that "The total number of simulation settings is 81". This provides specific experimental setup details. |