75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
import os
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import numpy as np
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import matplotlib.pyplot as plt
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def plot_learning_curve(scores, num_players, figure_path, n_episodes):
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plt.figure()
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plt.title("Running Average - Score")
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plt.xlabel("Episode")
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plt.ylabel("Score")
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plt.legend([f"Agent {num}" for num in range(num_players)])
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for score in scores:
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running_avg = np.zeros(len(score))
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for i in range(len(score)):
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if score[i] != 0:
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running_avg[i] = np.mean(score[max(0, i-int(n_episodes/10)):i+1])
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plt.plot(running_avg)
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plt.savefig(os.path.join(figure_path, "avg_score.png"))
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plt.close()
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def plot_avg_time(time_steps, num_players, fig_path):
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plt.figure()
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plt.title("Average Time Steps per Episode")
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for player in time_steps:
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plt.plot(player)
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plt.savefig(os.path.join(fig_path, 'avg_time.png'))
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plt.close()
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def plot_score(scores, num_players, figure_path):
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plt.figure()
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plt.title("Agent Rewards - No Averaging")
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plt.xlabel("Episode")
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plt.ylabel("Score")
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plt.legend([f"Agent {num}" for num in range(num_players)])
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for player_score in scores:
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plt.plot(player_score)
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plt.savefig(os.path.join(figure_path, 'score.png'))
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plt.close()
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def plot_loss(nn_type, losses, num_players, figure_path, n_episodes):
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plt.figure()
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plt.title(f"Running Average - {nn_type.capitalize()} Loss")
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plt.xlabel("Learning Iterations")
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plt.ylabel("Loss")
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plt.legend([f"Agent {num}" for num in range(num_players)])
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for loss in losses:
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running_avg = np.zeros(len(loss))
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for i in range(len(loss)):
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if loss[i] != 0:
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running_avg[i] = np.mean(loss[max(0, i-int(n_episodes/10)):(i+1)])
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plt.plot(running_avg)
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plt.savefig(os.path.join(figure_path, f"{nn_type}_loss.png"))
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plt.close()
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def plot_parameter(name, parameter, num_players, figure_path, n_episodes):
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plt.figure()
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plt.title(f"Running Average - {name.capitalize()}")
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plt.xlabel("Learning Iterations")
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plt.ylabel(f"{name.capitalize()}")
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plt.legend([f"Agent {num}" for num in range(num_players)])
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for param in parameter:
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running_avg = np.zeros(len(param))
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for i in range(len(param)):
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if param[i] != 0:
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running_avg[i] = np.mean(param[max(0, i-int(n_episodes/10)):(i+1)])
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plt.plot(running_avg)
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plt.savefig(os.path.join(figure_path, f"{name}.png"))
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plt.close()
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