Learning to Represent Action Values as a Hypergraph on the Action Vertices
Authors: Arash Tavakoli, Mehdi Fatemi, Petar Kormushev
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of our approach on a myriad of domains: illustrative prediction problems under minimal confounding effects, Atari 2600 games, and discretised physical control benchmarks. |
| Researcher Affiliation | Collaboration | Arash Tavakoli Imperial College London Mehdi Fatemi Microsoft Research Montréal Petar Kormushev Imperial College London |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code availability The source code can be accessed at: https://github.com/atavakol/ action-hypergraph-networks. |
| Open Datasets | Yes | We tested our approach in Atari 2600 games using the Arcade Learning Environment (ALE) (Bellemare et al., 2013). ... To test our approach in problems with larger action spaces, we consider a suite of physical control benchmarks simulated using Py Bullet (Coumans & Bai, 2019). |
| Dataset Splits | No | The paper refers to training and testing processes but does not explicitly provide details on validation splits or specific train/test/validation data partitioning proportions or methods within the main text or appendices. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'TensorFlow (Abadi et al., 2016) and NumPy (Harris et al., 2020)' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | Table 2: Hyperparameters used for HGQN and DQN in our physical control benchmarks. This table lists specific values for minibatch size, replay memory size, learning rate, optimiser (Adam), etc. |