| # Final Results Plot |
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| We use saved results by `.npz` type (Our algorithm, and CRM) and `.json` type (QRM). Some experiments include saved files as `RLAlgorithm.zip`, and saved results (success rate, mean reward, episode length, partial success) as `.npz` files. |
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|
| - **normal**: This folder includes experimental results for a deterministic environment without noise, and infeasible conditions. |
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| - **noise01**: This folder contains experimental results for a noisy environment without infeasible conditions. |
| |
| - **missing**: This folder contains experimental results for an infeasible environment without noise. |
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| - **normal_csv**: Contains `.csv` files to plot results in environments without noise and infeasible conditions. |
| |
| - **noise_csv**: Contains `.csv` files to plot results in noisy environments without infeasible conditions. |
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| - **missing_csv**: Contains `.csv` files to plot results in infeasible environments without noise. |
| |
| ### Plotting Results: |
| |
| 1. Convert `.npz` and `.json` file results into `.csv` file results for each environment (deterministic, noisy, missing). |
| |
| 2. Use `draw_with_csv.py` to plot results for each environment. |
| |
| 3. **Optional**: To plot results on the HalfCheetah environment for the A2C and PPO algorithm, use `draw_cheetah.py`. |
| |
| ## compute_mean_std.py |
| |
| This file aims to compute the mean reward and standard deviation saved by `npz` files for the logic-based reward shaping algorithm and CRM, and the `json` file for QRM. Computed mean rewards and standard deviations will be saved as a `csv` file. |
| |
| Arguments: |
| - First argument relates to the `missing` condition, which is boolean. |
| - Second argument relates to the `noise` condition and is a float. We've tested with a 0.1 (10%) chance of noisy control. |
| |
| Example: |
| ```bash |
| python compute_mean_std.py False 0 |
| ``` |
| |
| The example above converts saved results (either `.npz` or `.json` type) in a deterministic environment with a 0% chance of noisy control for various algorithms across different environments. |
| ```bash |
| python compute_mean_std.py False 0 |
| ``` |
| This example converts saved results of the A2C algorithm on the HalfCheetah domain. |
| |
| ## draw_with_csv.py |
| This will read the converted `csv` file by `compute_mean_std.py`, then plot and save the results. |
| |
| Arguments: |
| - First argument relates to type of plots (normal, noise, missing) |
| |
| To see the result on deterministic environments without noise and infeaisble condition, |
| Example: |
| ```bash |
| python draw_with_csv.py normal |
| ``` |
| |
| To see the result on environments with noise and without infeaisble condition, |
| Example: |
| ```bash |
| python draw_with_csv.py noise |
| ``` |
| |
| To see the result on environments without noise and with infeaisble condition, |
| Example: |
| ```bash |
| python draw_with_csv.py missing |
| ``` |
| |
| All plots will be saved `./saved_plots` folder. |
| |
| ## draw_cheetah.py |
| This is exclusively for the DDPG, A2C, PPO algorithm's results on the HalfCheetah Environment. |
| |
| Once you have converted all saved results (.npz files) into CSV format with `compute_mea_std.py`, |
| use |
| ```bash |
| python draw_cheetah.py |
| ``` |
| |
| All plots will be saved `./saved_plots` folder. |
| ## plotter.py |
| This includes functions to aggregate all different types of results (QRM result is saved with `.json`, and the others are saved with `.npz`). |
| These functions will not be used directly. Instead, these functions will be used when we convert .npz and .json format to CSV format with `compute_mea_std.py`. |
