Informed Expectations to Guide GDA Agents in Partially Observable Environments
Authors: Dustin Dannenhauer, Hector Munoz-Avila, Michael T. Cox
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a formalism of the problem that includes sensing costs, a GDA algorithm using this formalism, an examination of four methods of expectations under this formalism, and an implementation of the algorithm and empirical study. |
| Researcher Affiliation | Academia | Dustin Dannenhauer and Hector Munoz-Avila Dept. of Computer Science and Engineering Lehigh University, Bethlehem, PA USA dtd212,hem4@lehigh.edu Michael T. Cox Wright State Research Institute Wright State University, Dayton, OH michael.cox@wright.edu |
| Pseudocode | Yes | Algorithm 1 shows the pseudo-code for our agent that is operating in a partially observable and dynamic environment. |
| Open Source Code | No | The paper does not contain any statement about releasing the source code for the methodology or provide a link to a code repository. |
| Open Datasets | No | The paper mentions 'implemented two simulated environments, marsworld and blockscraft' but these are custom simulations described in the paper, not publicly available datasets with access information. |
| Dataset Splits | No | The paper mentions running '1000 random scenarios' but does not specify any training, validation, or test dataset splits, nor does it refer to predefined standard splits. |
| Hardware Specification | No | The paper describes the simulated environments and algorithms but does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions implementing simulated environments and an algorithm but does not list any specific software or library names with version numbers (e.g., Python, PyTorch, Java). |
| Experiment Setup | Yes | In Figure 1 the chance of failure per action executed was 20% for beacons, fires, and flares each. In Figure 2, the chance that a block would be removed was 10% and the chance that a block would be added was 30%. |