Updated metrics correctly
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1ab8df01ea
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2 changed files with 11 additions and 11 deletions
10
main.py
10
main.py
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@ -185,17 +185,17 @@ def main():
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print(f"Models saved to {chkpt_path}")
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metrics.plot_learning_curve(score_history, parsed_args.n_agents, figure_path)
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metrics.plot_learning_curve(score_history, parsed_args.n_agents, figure_path, n_episodes)
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metrics.plot_score(score_history, parsed_args.n_agents, figure_path)
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metrics.plot_loss('actor', actor_loss, parsed_args.n_agents, figure_path)
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metrics.plot_loss('actor', actor_loss, parsed_args.n_agents, figure_path, n_episodes)
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metrics.plot_loss('critic', critic_loss, parsed_args.n_agents, figure_path)
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metrics.plot_loss('critic', critic_loss, parsed_args.n_agents, figure_path, n_episodes)
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metrics.plot_parameter('entropy', entropy, parsed_args.n_agents, figure_path)
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metrics.plot_parameter('entropy', entropy, parsed_args.n_agents, figure_path, n_episodes)
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metrics.plot_parameter('advantage', advantage, parsed_args.n_agents, figure_path)
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metrics.plot_parameter('advantage', advantage, parsed_args.n_agents, figure_path, n_episodes)
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metrics.plot_avg_time(time_alive, parsed_args.n_agents, figure_path)
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@ -3,7 +3,7 @@ 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, ep_lenght):
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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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@ -13,7 +13,7 @@ def plot_learning_curve(scores, num_players, figure_path, ep_lenght):
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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-int(ep_length/10)):(i+1)])
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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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@ -40,7 +40,7 @@ def plot_score(scores, num_players, figure_path):
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plt.close()
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def plot_loss(nn_type, losses, num_players, figure_path, ep_length):
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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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@ -50,13 +50,13 @@ def plot_loss(nn_type, losses, num_players, figure_path, ep_length):
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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-int(ep_length/10)):(i+1)])
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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, ep_length):
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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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@ -66,7 +66,7 @@ def plot_parameter(name, parameter, num_players, figure_path, ep_length):
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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-int(ep_length/10)):(i+1)])
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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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