Re-understanding Finite-State Representations of Recurrent Policy Networks
Authors: Mohamad H Danesh, Anurag Koul, Alan Fern, Saeed Khorram
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our case studies on 7 Atari games and 3 control benchmarks demonstrate that the approach can reveal insights that have not been previously noticed. |
| Researcher Affiliation | Academia | 1Department of EECS, Oregon State University, Corvallis, OR, USA. Correspondence to: Mohamad H. Danesh <daneshm@oregonstate.edu>. |
| Pseudocode | Yes | To do this, we conduct a simple form of functional pruning (details and pseudo-code in appendix), to identify and prune unnecessary branches at decision points. |
| Open Source Code | Yes | Source code is available at: github.com/modanesh/Differential_IG |
| Open Datasets | Yes | We consider 7 deterministic Atari environments: Bowling, Boxing, Breakout, Ms Pacman, Pong, Sea Quest and Space Invaders, and 3 stochastic discrete-action classic control tasks: Acrobot, Cart Pole, and Lunar Lander. |
| Dataset Splits | No | The paper describes the environments and discusses performance but does not specify dataset splits (e.g., percentages or counts for training, validation, or test sets) for reproducibility. |
| Hardware Specification | No | The paper does not explicitly mention specific hardware components (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms like A3C and R2D2, and approaches like Integrated Gradient, but does not provide specific version numbers for any software libraries or tools used (e.g., PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | No | Detailed information on these choices along with hyperparameter choices are in the appendix. |