| import argparse | |
| import numpy as np | |
| import os | |
| from matplotlib import pyplot as plt | |
| def calc_stats(filepath): | |
| # load the numpy file | |
| data = np.load(filepath)["results"] | |
| # sort the arrays and delete the first and last elements | |
| data = np.sort(data, axis=1) | |
| data = np.delete(data, -1, axis=1) | |
| data = np.delete(data, 0, axis=1) | |
| avg = round(np.mean(data), 2) | |
| std = round(np.std(data), 2) | |
| return avg, std | |
| # parser = argparse.ArgumentParser() | |
| # parser.add_argument("-f", "--filepath", required=True, help="Specify the file path to the agent.", type=str) | |
| # args = parser.parse_args() | |
| # Get the file paths and store in list. | |
| # For each file path, I want to calculate the mean reward. This would be the mean reward for the training run over all evaluations. | |
| # For each file path, append the mean reward to an averages list | |
| # Plot the averages! | |
| filepaths = [] | |
| for d in os.listdir("agents/"): | |
| if "dqn_v2" in d: | |
| path = "agents/" + d + "/evaluations.npz" | |
| filepaths.append(path) | |
| means = [] | |
| stds = [] | |
| for path in filepaths: | |
| avg, std = calc_stats(path) | |
| means.append(avg) | |
| stds.append(std) | |
| runs = [] | |
| for i in range(len(filepaths)): | |
| runs.append(i + 1) | |
| plt.xlabel("training runs") | |
| plt.ylabel("score") | |
| plt.bar(runs, means) | |
| plt.bar(runs, stds) | |
| plt.legend(["Mean evaluation score", "Standard deviation"]) | |
| plt.title("Average Evaluation Score and Standard Deviation\nAdjusted for Outliers\nAgent: dqn_v2") | |
| plt.show() | |