93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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def generate(parsed_args):
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# Setup parameter monitoring
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score_history = np.zeros(
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shape=(parsed_args.n_agents, parsed_args.n_episodes))
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best_score = np.zeros(parsed_args.n_agents)
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actor_loss = np.zeros(shape=(parsed_args.n_agents,
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parsed_args.n_episodes))
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critic_loss = np.zeros(shape=(parsed_args.n_agents,
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parsed_args.n_episodes))
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total_loss = np.zeros(shape=(parsed_args.n_agents,
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parsed_args.n_episodes))
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entropy = np.zeros(shape=(parsed_args.n_agents,
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parsed_args.n_episodes))
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advantage = np.zeros(shape=(parsed_args.n_agents,
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parsed_args.n_episodes))
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return score_history, best_score, actor_loss,
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critic_loss, total_loss, entropy,
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advantage
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def plot_learning_curve(scores, num_players, figure_path):
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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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running_avg[i] = np.mean(score[max(0, i-100):(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_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):
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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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running_avg[i] = np.mean(loss[max(0, i-100):(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):
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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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running_avg[i] = np.mean(param[max(0, i-100):(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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