Linear Feature Encoding for Reinforcement Learning
Authors: Zhao Song, Ronald E. Parr, Xuejun Liao, Lawrence Carin
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments The goal of our experiments is to show that the model of and algorithms for feature encoding presented above are practical and effective. Figure 1 shows the results with means and 95% confidence intervals, given different numbers of training episodes, where Encoder τ corresponds to the version of Algorithm 1 with τ changes in the encoder. |
| Researcher Affiliation | Academia | Zhao Song, Ronald Parr , Xuejun Liao, Lawrence Carin Department of Electrical and Computer Engineering Department of Computer Science Duke University, Durham, NC 27708, USA |
| Pseudocode | Yes | Algorithm 1 Iterative Learning of Encoder and Policy. Algorithm 2 Linear Feature Discovery. |
| Open Source Code | No | All code is written in MATLAB and tested on a machine with 3.1GHz CPU and 8GB RAM. No explicit statement of public release or link to code repository. |
| Open Datasets | Yes | To represent raw states for the encoder, we use three concatenated sampled MNIST digits and hence a raw state is a 28 × 28 × 3 = 2352 dimensional vector. |
| Dataset Splits | Yes | For the encoder, the number of features k is selected over the validation set to achieve the best performance. |
| Hardware Specification | Yes | All code is written in MATLAB and tested on a machine with 3.1GHz CPU and 8GB RAM. |
| Software Dependencies | No | All code is written in MATLAB and tested on a machine with 3.1GHz CPU and 8GB RAM. No specific MATLAB version or other software dependencies with versions are listed. |
| Experiment Setup | Yes | The discount factor is set to be 0.95. ... We used k = 50 features for both linear encoder and random projection. ... We set k = 203 features for the linear encoder. |