Game Engine Learning from Video
Authors: Matthew Guzdial, Boyang Li, Mark O. Riedl
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present results from two distinct evaluations meant to demonstrate the utility of our system. [...] For the first evaluation we compare the frames predicted by our learned engine against a naive baseline and a Convolutional Neural Net (CNN)... For the second evaluation we evaluate the application of the learned engine s knowledge to training a game playing agent... We present the results of this evaluation in Figure 3. [...] we find that our learned engine predicts frames significantly more similar to the true frame (Wilxocon paired test, p < 2.2e 16). |
| Researcher Affiliation | Academia | Matthew Guzdial, Boyang Li, Mark O. Riedl School of Interactive Computing, Georgia Institute of Technology mguzdial3@gatech.edu, boyangli@gatech.edu, riedl@cc.gatech.edu |
| Pseudocode | Yes | Algorithm 1: frame scan; Algorithm 2: Engine Search |
| Open Source Code | No | No, the paper does not explicitly state that its own methodology's source code is available or provide a link to a repository. It mentions using 'Open CV [Pulli et al., 2012], an open-source machine vision toolkit', but this refers to a third-party tool, not the authors' own implementation. |
| Open Datasets | Yes | We made use of gameplay footage that had been evaluated in prior work by Summerville et al. [2016]. In particular we drew on the two most different players and their respective gameplay videos which Summerville et al. describe as the speedrunner and explorer. |
| Dataset Splits | No | No, the paper mentions a 'common train and test procedure' and that the CNN was 'trained on the same training data' and that 'performance on the test set converged', but it does not explicitly define a separate validation dataset or its split. |
| Hardware Specification | Yes | While this is an offline process, meant to only run once, the running time can still be prohibitive with lower values of θ (running for up to two weeks for a single game level on a 2013 i Mac). |
| Software Dependencies | No | No, the paper mentions using 'Open CV [Pulli et al., 2012]' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | In addition we set θ to zero, to ensure the best possible output engine. [...] We made use of the same convolutional neural network architecture described in the Ranzato et al. paper, a network with four convolutional layers with 128 filters each, each making use of relu activation. [...] Thus we went from 416x364 pixel RGB images to 104x91 pixel grayscale images... |