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) # parser.add_argument("-s", "--save", help="Specify whether to save the chart.", action="store_const", const=True) # 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 Agent: dqn_v2") plt.show() # plt.savefig("charts/fig1")