Fast Convergence of Regularized Learning in Games
Authors: Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we simulate a 4-bidder simultaneous auction game, and compare our optimistic algorithms against Hedge [7] in terms of utilities, regrets and convergence to equilibria. We run the game for n = 4 bidders and m = 4 items and valuation v = 20. The bids are discretized to be any integer in [1, 20]. We find that the sum of the regrets and the maximum individual regret of each player are remarkably lower under Optimistic Hedge as opposed to Hedge. In Figure 1 we plot the maximum individual regret as well as the sum of the regrets under the two algorithms, using = 0.1 for both methods. |
| Researcher Affiliation | Collaboration | Vasilis Syrgkanis Microsoft Research New York, NY vasy@microsoft.com Alekh Agarwal Microsoft Research New York, NY alekha@microsoft.com Haipeng Luo Princeton University Princeton, NJ haipengl@cs.princeton.edu Robert E. Schapire Microsoft Research New York, NY schapire@microsoft.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes a simulated auction game without providing concrete access information (link, DOI, repository, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes a simulation of game dynamics for 10,000 rounds but does not provide specific dataset split information (percentages, sample counts, or detailed splitting methodology) for reproducibility. |
| Hardware Specification | No | The paper mentions simulating a game but does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms like 'Optimistic Hedge' and 'Hedge' but does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We run the game for n = 4 bidders and m = 4 items and valuation v = 20. The bids are discretized to be any integer in [1, 20]. We find that the sum of the regrets and the maximum individual regret of each player are remarkably lower under Optimistic Hedge as opposed to Hedge. In Figure 1 we plot the maximum individual regret as well as the sum of the regrets under the two algorithms, using = 0.1 for both methods. |