Model-Based Episodic Memory Induces Dynamic Hybrid Controls
Authors: Hung Le, Thommen Karimpanal George, Majid Abdolshah, Truyen Tran, Svetha Venkatesh
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our model allows significantly faster and better learning than other strong reinforcement learning agents across a variety of environments including stochastic and non-Markovian settings. |
| Researcher Affiliation | Academia | Hung Le, Thommen Karimpanal George, Majid Abdolshah, Truyen Tran, Svetha Venkatesh Applied AI Institute, Deakin University, Geelong, Australia thai.le@deakin.edu.au |
| Pseudocode | Yes | Algorithm 1 MBEC++: Complementary reinforcement learning with MBEC and DQN. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for the methodology described in this paper, nor does it provide a direct link to such a repository. |
| Open Datasets | Yes | We consider 3 classical problems: Cart Pole, Mountain Car and Lunar Lander. |
| Dataset Splits | No | The paper mentions training models and using replay buffers, but it does not specify explicit training/validation/test dataset splits (e.g., percentages or counts) for the environments used in the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like DQN and LSTM implementations, but does not provide specific version numbers for any key software dependencies or libraries. |
| Experiment Setup | Yes | Details of the baseline configurations and hyper-parameter tuning for each tasks can be found in Appendix B. |