Regret Transfer and Parameter Optimization
Authors: Noam Brown, Tuomas Sandholm
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments in no-limit Leduc Hold em and nolimit Texas Hold em to optimize bet sizing. This amounts to the first action abstraction algorithm (algorithm for selecting a small number of discrete actions to use from a continuum of actions a key preprocessing step for solving large games using current equilibrium-finding algorithms) with convergence guarantees for extensive-form games. |
| Researcher Affiliation | Academia | Noam Brown Robotics Institute Carnegie Mellon University noamb@cs.cmu.edu Tuomas Sandholm Computer Science Department Carnegie Mellon University sandholm@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Parameter optimization in two-player zero-sum games |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use Leduc hold em poker (Southey et al. 2005) as the test problem here. It has become a common testbed because the game tree is rather large, but small enough that exploitability of a strategy can be computed, and thereby solution quality can be measured. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper mentions |
| Software Dependencies | No | The paper mentions using the |
| Experiment Setup | Yes | We used a learning rate ls = s 3 4 , α = 50, and K = 100. We conducted three runs of the algorithm, starting from three different initial values for θ2, respectively. |