Pneuma/pneuma-pygame
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This commit is contained in:
Vasilis Valatsos 2024-03-04 17:26:11 +02:00
parent 4cc7f6366e
commit e57d826239
2 changed files with 24 additions and 6 deletions
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18
main.py
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@ -101,6 +101,12 @@ def main():
episode_total_loss = np.zeros( episode_total_loss = np.zeros(
shape=(n_agents, learnings_per_episode)) shape=(n_agents, learnings_per_episode))
episode_entropy = np.zeros(
shape=(n_agents, learnings_per_episode))
episode_advantage = np.zeros(
shape=(n_agents, learnings_per_episode))
# Main game loop # Main game loop
for step in tqdm(range(episode_length), for step in tqdm(range(episode_length),
leave=False, leave=False,
@ -127,6 +133,12 @@ def main():
episode_total_loss[player.player_id][learn_iters % learnings_per_episode]\ episode_total_loss[player.player_id][learn_iters % learnings_per_episode]\
= player.agent.total_loss = player.agent.total_loss
episode_entropy[player.player_id][learn_iters % learnings_per_episode]\
= player.agent.entropy
episode_advantage[player.player_id][learn_iters % learnings_per_episode]\
= player.agent.advantage
learn_iters += 1 learn_iters += 1
@ -151,6 +163,12 @@ def main():
time_alive[player.player_id][episode] = step time_alive[player.player_id][episode] = step
entropy[player.player_id][episode] = np.mean(
episode_entropy)
advantage[player.player_id][episode] = np.mean(
episode_advantage)
# Check for new best score # Check for new best score
if score > best_score[player.player_id]: if score > best_score[player.player_id]:
print(f"\nEpisode:\ print(f"\nEpisode:\

12
utils/metrics.py
View file

@ -3,7 +3,7 @@ import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
def plot_learning_curve(scores, num_players, figure_path): def plot_learning_curve(scores, num_players, figure_path, ep_lenght):
plt.figure() plt.figure()
plt.title("Running Average - Score") plt.title("Running Average - Score")
@ -13,7 +13,7 @@ def plot_learning_curve(scores, num_players, figure_path):
for score in scores: for score in scores:
running_avg = np.zeros(len(score)) running_avg = np.zeros(len(score))
for i in range(len(score)): for i in range(len(score)):
running_avg[i] = np.mean(score[max(0, i-100):(i+1)]) running_avg[i] = np.mean(score[max(0, i-int(ep_length/10)):(i+1)])
plt.plot(running_avg) plt.plot(running_avg)
plt.savefig(os.path.join(figure_path, "avg_score.png")) plt.savefig(os.path.join(figure_path, "avg_score.png"))
plt.close() plt.close()
@ -40,7 +40,7 @@ def plot_score(scores, num_players, figure_path):
plt.close() plt.close()
def plot_loss(nn_type, losses, num_players, figure_path): def plot_loss(nn_type, losses, num_players, figure_path, ep_length):
plt.figure() plt.figure()
plt.title(f"Running Average - {nn_type.capitalize()} Loss") plt.title(f"Running Average - {nn_type.capitalize()} Loss")
@ -50,13 +50,13 @@ def plot_loss(nn_type, losses, num_players, figure_path):
for loss in losses: for loss in losses:
running_avg = np.zeros(len(loss)) running_avg = np.zeros(len(loss))
for i in range(len(loss)): for i in range(len(loss)):
running_avg[i] = np.mean(loss[max(0, i-100):(i+1)]) running_avg[i] = np.mean(loss[max(0, i-int(ep_length/10)):(i+1)])
plt.plot(running_avg) plt.plot(running_avg)
plt.savefig(os.path.join(figure_path, f"{nn_type}_loss.png")) plt.savefig(os.path.join(figure_path, f"{nn_type}_loss.png"))
plt.close() plt.close()
def plot_parameter(name, parameter, num_players, figure_path): def plot_parameter(name, parameter, num_players, figure_path, ep_length):
plt.figure() plt.figure()
plt.title(f"Running Average - {name.capitalize()}") plt.title(f"Running Average - {name.capitalize()}")
@ -66,7 +66,7 @@ def plot_parameter(name, parameter, num_players, figure_path):
for param in parameter: for param in parameter:
running_avg = np.zeros(len(param)) running_avg = np.zeros(len(param))
for i in range(len(param)): for i in range(len(param)):
running_avg[i] = np.mean(param[max(0, i-100):(i+1)]) running_avg[i] = np.mean(param[max(0, i-int(ep_length/10)):(i+1)])
plt.plot(running_avg) plt.plot(running_avg)
plt.savefig(os.path.join(figure_path, f"{name}.png")) plt.savefig(os.path.join(figure_path, f"{name}.png"))
plt.close() plt.close()