Statistics and Samples in Distributional Reinforcement Learning
Authors: Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare EDRL with existing methods on a variety of MDPs to illustrate concrete aspects of our analysis, and develop a deep RL variant of the algorithm, ER-DQN, which we evaluate on the Atari-57 suite of games. |
| Researcher Affiliation | Industry | 1Deep Mind 2Google Brain. Correspondence to: Mark Rowland <markrowland@google.com>. |
| Pseudocode | Yes | Algorithm 1 Generic DRL update algorithm. Algorithm 2 Stochastic EDRL update algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate ER-DQN on the Arcade Learning Environment (Bellemare et al., 2013). |
| Dataset Splits | No | The paper mentions evaluating on the Atari-57 suite but does not specify concrete train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU/CPU models, memory, or cloud computing instance types used for running experiments. |
| Software Dependencies | No | The paper mentions using a 'Sci Py optimisation routine', but does not provide a specific version number for SciPy or any other software dependencies. |
| Experiment Setup | Yes | Precise experimental details and results are given in Appendix Section D. |