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| author | Loïc Guégan <loic.guegan@mailbox.org> | 2025-09-22 11:23:07 +0200 |
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| committer | Loïc Guégan <loic.guegan@mailbox.org> | 2025-09-22 11:23:07 +0200 |
| commit | e8b9ee5b5efb85f30d7931dd295bc2e2cb4ff8ad (patch) | |
| tree | 48df32e584c00c4e9baf649b1eba8be010fa591e | |
| parent | 0b305228c7720695cd5bfc32053a41e941ac81e9 (diff) | |
Update readme
| -rw-r--r-- | analysis/README.md | 3 |
1 files changed, 2 insertions, 1 deletions
diff --git a/analysis/README.md b/analysis/README.md index 47ce5c0..75b0ac5 100644 --- a/analysis/README.md +++ b/analysis/README.md @@ -1,11 +1,12 @@ # Analysis ## Files -- analysis.R: Here to test various data analysis +- analysis.R: Do some analysis and generate Table IV by comparing coverage and energy from simulation results to random targets - kmeans.R: Just here for doing some tests - offline.R: contains two major functions: - build_models: To generate K-fold cross-validation results (note that hyper-parameters for decisions tree is fixed (no validation set)) - generate_inputs: generate the inputs for the simulations experiments + the decision tree plots + - Generate Fig 5 (decision tree nodes) - in-situ.R: Implement the in-situ learning approach (Figure 4a 4b and 4c) - For figure 4a and 4b we train the model with increasing amount of data from previous results as if we were using one policy per day (see section IV.A) - For figure 4c, delta is generated by comparing using each policies in round-robin (one per days to perform the training) |