Mix & Match Agent Curricula for Reinforcement Learning
Authors: Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now test and analyse our method on three sets of RL experiments. We train all agents with a form of batched actor critic with an off policy correction (Espeholt et al., 2018) using Deep Mind Lab (Beattie et al., 2016) as an environment suite. |
| Researcher Affiliation | Industry | 1Deep Mind, London, UK. Correspondence to: W Czarnecki <lejlot@google.com>, S Jayakumar <sidmj@google.com>. |
| Pseudocode | No | The paper describes its methods using prose, equations, and diagrams, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or include a link to a code repository for the methodology described. |
| Open Datasets | Yes | All agents with a form of batched actor critic with an off policy correction (Espeholt et al., 2018) using Deep Mind Lab (Beattie et al., 2016) as an environment suite. |
| Dataset Splits | No | The paper uses the Deep Mind Lab environment for experiments, which is an interactive environment rather than a static dataset. Therefore, it does not provide explicit training/validation/test dataset splits in the manner of supervised learning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running its experiments. |
| Software Dependencies | No | The paper mentions environments and algorithms used (e.g., Deep Mind Lab, A3C), but it does not specify software dependencies with version numbers (e.g., specific versions of deep learning frameworks or programming languages). |
| Experiment Setup | No | The paper states that 'Full details of the experimental hyper-parameters can be found in the appendix', but these details are not present in the provided main text of the paper. |