ledmands
Modified plot_evaluations.py with experimental labels and scaling. Still needs more tuning.
e64054f
from numpy import load | |
import matplotlib.pyplot as plt | |
# import matplotlib.axes | |
filepath = "dqn_v2-5/evaluations.npz" | |
data = load(filepath) | |
lst = data.files # data.files lists the keys that are available for data | |
# print('ep_lengths: \n', data['ep_lengths']) | |
# results and ep_lengths are 2d arrays, because each evaluation is 5 episodes long. | |
# I want to plot the average of each evaluation. | |
# for each item in results, loop through the array and save the average | |
avg_ep_result_arr = [] | |
for eval in data['results']: | |
result_sum = 0 | |
for result in eval: | |
result_sum = result_sum + result | |
avg_ep_result = result_sum / len(eval) | |
avg_ep_result_arr.append(avg_ep_result) | |
avg_ep_len_arr = [] | |
for eval in data['ep_lengths']: | |
max_len = 0 | |
y_limit = 0 | |
ep_len_sum = 0 | |
for ep_length in eval: | |
ep_len_sum = ep_len_sum + ep_length | |
if ep_length > max_len: | |
max_len = ep_length | |
if ep_length > y_limit and y_limit < max_len: | |
y_limit = ep_length | |
avg_ep_len = ep_len_sum / len(eval) | |
avg_ep_len_arr.append(avg_ep_len) | |
y_limit = y_limit * 1.9 | |
x = plt.plot(data['timesteps'], avg_ep_result_arr) | |
# plt.bar(data['timesteps'], avg_ep_len_arr, width=10000) | |
y = plt.plot(data['timesteps'], avg_ep_len_arr) | |
plt.ylim(top=y_limit) | |
# plt.ylabel("Avg ep score") | |
# lineObjects = plt.plot(x, y) | |
plt.legend(["avg ep result", "avg ep length"]) | |
plt.title("result and length over steps\nfilepath: " + filepath) | |
plt.show() |