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torch_plot_DDqn.py
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import matplotlib.pyplot as plt
import json
import os
import numpy as np
# Define the hyperparameters for each model you want to compare
eps_values = [1.0, 0.999, 0.9] # nominal is 1.0
lr_values = [0.01, 0.001, 0.0001] # nominal is 0.0001
eps_dec_values = [0.90, 0.995, 0.999] # nominal is 0.995
batch_size_values = [64, 128, 256] # nominal is 128
n_episodes = 1500 # Use the same number of episodes for all models
window_size = 50 # The window size for calculating the moving average
rolling_window = 100 # The window size for calculating the rolling avera
# Set up folder and file naming conventions
FOLDER_NAME = 'torch_DDqn'
SCORES_FILENAME_TEMPLATE = f'{FOLDER_NAME}/ddqn_scores_{{episodes}}_eps_{{eps}}_eps_d_{{eps_d}}_bs_{{bs}}_lr_{{lr}}.json'
#########################################################
# Define the other hyperparameters for the models #######
# Comment the one which is being compared ###############
#########################################################
epsilon = 1.0
lr = 0.001
batch_size = 128
# epsilon_dec = 0.995
#########################################################
##################### EPSILON ###########################
#########################################################
# # Prepare to plot
# plt.figure(figsize=(12, 6))
# for eps in eps_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=eps,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Plot
# plt.plot(range(1, n_episodes + 1), scores, label=f'Eps={eps}', marker='o')
# # Finalize the plot
# plt.xlabel('Episode')
# plt.ylabel('Reward')
# plt.title(f'Reward per Episode for different models with \n(epsilon_decrement={epsilon_dec}, Batch Size={batch_size}, Learning Rate={lr})')
# plt.legend()
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_epsilon_comparison/reward_per_episode_comparison_eps-{n_episodes}_eps_d_{epsilon_dec}_bs_{batch_size}_lr_{lr}.png'
# plt.savefig(plot_filename)
# print(f"Plot saved to: {plot_filename}")
# plt.show()
# ### Average reward plot
# # Prepare to plot
# plt.figure(figsize=(12, 6))
# for eps in eps_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=eps,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Calculate the moving average for each window of 50 episodes
# avg_scores = []
# x_ticks = []
# for i in range(0, len(scores), window_size):
# window_avg = np.mean(scores[i:i + window_size])
# avg_scores.append(window_avg)
# x_ticks.append(i + window_size) # x values should be at the end of each window
# # Plot
# plt.plot(x_ticks, avg_scores, label=f'Eps={eps}', marker='o')
# # Finalize the plot
# plt.xlabel('Episode')
# plt.ylabel(f'Average Reward (over {window_size} episodes)')
# plt.title(f'Reward Average per {window_size} Episodes with \n(epsilon_decrement={epsilon_dec}, Batch Size={batch_size}, Learning Rate={lr})')
# plt.legend()
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_epsilon_comparison/reward_avg_per_{window_size}_episodes_comparison_eps-{n_episodes}_eps_d_{epsilon_dec}_bs_{batch_size}_lr_{lr}.png'
# plt.savefig(plot_filename)
# print(f"Plot saved to: {plot_filename}")
# plt.show()
# ##############################
# ### rolling average
# plt.figure(figsize=(12, 6))
# plt.figure(figsize=(12, 6))
# for eps in eps_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=eps,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Calculate the rolling average of rewards
# rolling_avg_scores = np.convolve(scores, np.ones(rolling_window)/rolling_window, mode='valid')
# # Plot the rolling average
# plt.plot(range(rolling_window, len(scores) + 1), rolling_avg_scores, label=f'epsilon={eps}', linestyle='--')
# # Finalize the plot
# plt.xlabel('Episode', fontsize=17)
# plt.ylabel(f'Rolling Average Reward (over {rolling_window} episodes)', fontsize=17)
# plt.title(f'Rolling Reward Average per {rolling_window} Episodes: eps comparison with \n(epsilon_dec ={epsilon_dec}, Batch Size={batch_size}, Learning Rate={lr})', fontsize=17)
# plt.legend(fontsize=17)
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_epsilon_comparison/reward_rolling_avg_per_{rolling_window}_episodes_comparison_eps-{n_episodes}_eps_dec_{epsilon_dec}_bs_{batch_size}_lr_{lr}.png'
# plt.savefig(plot_filename, bbox_inches='tight')
# print(f"Plot saved to: {plot_filename}")
# plt.show()
#########################################################
#################### LEARNING RATE ######################
#########################################################
# # Prepare to plot
# plt.figure(figsize=(12, 6))
# for lr in lr_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=epsilon,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Plot
# plt.plot(range(1, n_episodes + 1), scores, label=f'Learning Rate={lr}', marker='o')
# # Finalize the plot
# plt.xlabel('Episode')
# plt.ylabel('Reward')
# plt.title(f'Reward per Episode for different models with \n(epsilon ={epsilon}, epsilon_decrement={epsilon_dec}, Batch Size={batch_size})')
# plt.legend()
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_lr_comparison/reward_per_episode_comparison_lr-{n_episodes}_eps_{epsilon}_eps_d_{epsilon_dec}_bs_{batch_size}.png'
# plt.savefig(plot_filename)
# print(f"Plot saved to: {plot_filename}")
# plt.show()
# ### Average reward plot
# # Prepare to plot
# plt.figure(figsize=(12, 6))
# for lr in lr_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=epsilon,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Calculate the moving average for each window of 50 episodes
# avg_scores = []
# x_ticks = []
# for i in range(0, len(scores), window_size):
# window_avg = np.mean(scores[i:i + window_size])
# avg_scores.append(window_avg)
# x_ticks.append(i + window_size) # x values should be at the end of each window
# # Plot
# plt.plot(x_ticks, avg_scores, label=f'Learning Rate={lr}', marker='o')
# # Finalize the plot
# plt.xlabel('Episode')
# plt.ylabel(f'Average Reward (over {window_size} episodes)')
# plt.title(f'Reward Average per {window_size} Episodes with \n(epsilon ={epsilon}, epsilon_decrement={epsilon_dec}, Batch Size={batch_size})')
# plt.legend()
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_lr_comparison/reward_avg_per_{window_size}_episodes_comparison_lr-{n_episodes}_eps_{epsilon}_eps_d_{epsilon_dec}_bs_{batch_size}.png'
# plt.savefig(plot_filename)
# print(f"Plot saved to: {plot_filename}")
# plt.show()
# ##############################
# ### rolling average
# plt.figure(figsize=(12, 6))
# for lr in lr_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=epsilon,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Calculate the rolling average of rewards
# rolling_avg_scores = np.convolve(scores, np.ones(rolling_window)/rolling_window, mode='valid')
# # Plot the rolling average
# plt.plot(range(rolling_window, len(scores) + 1), rolling_avg_scores, label=f'Learning Rate={lr}', linestyle='--')
# # Finalize the plot
# plt.xlabel('Episode', fontsize=17)
# plt.ylabel(f'Rolling Average Reward (over {rolling_window} episodes)', fontsize=17)
# plt.title(f'Rolling Reward Average per {rolling_window} Episodes: Learning Rate comparison with \n(epsilon ={epsilon}, epsilon_decrement={epsilon_dec}, Batch Size={batch_size})', fontsize=17)
# plt.legend(fontsize=17)
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_lr_comparison/reward_rolling_avg_per_{rolling_window}_episodes_comparison_lr-{n_episodes}_eps_{epsilon}_eps_d_{epsilon_dec}_bs_{batch_size}.png'
# plt.savefig(plot_filename, bbox_inches='tight')
# print(f"Plot saved to: {plot_filename}")
# plt.show()
#########################################################
###################### BATCH SIZE #######################
#########################################################
# # Prepare to plot
# plt.figure(figsize=(12, 6))
# for batch_size in batch_size_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=epsilon,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Plot
# plt.plot(range(1, n_episodes + 1), scores, label=f'Batch Size={batch_size}', marker='o')
# # Finalize the plot
# plt.xlabel('Episode')
# plt.ylabel('Reward')
# plt.title(f'Reward per Episode for different models with \n(epsilon ={epsilon}, epsilon_decrement={epsilon_dec}, Learning Rate={lr})')
# plt.legend()
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_bs_comparison/reward_per_episode_comparison_bs-{n_episodes}_eps_{epsilon}_eps_d_{epsilon_dec}_lr_{lr}.png'
# plt.savefig(plot_filename)
# print(f"Plot saved to: {plot_filename}")
# plt.show()
# ### Average reward plot
# # Prepare to plot
# plt.figure(figsize=(12, 6))
# for batch_size in batch_size_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=epsilon,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Calculate the moving average for each window of 50 episodes
# avg_scores = []
# x_ticks = []
# for i in range(0, len(scores), window_size):
# window_avg = np.mean(scores[i:i + window_size])
# avg_scores.append(window_avg)
# x_ticks.append(i + window_size) # x values should be at the end of each window
# # Plot
# plt.plot(x_ticks, avg_scores, label=f'Batch Size={batch_size}', marker='o')
# # Finalize the plot
# plt.xlabel('Episode')
# plt.ylabel(f'Average Reward (over {window_size} episodes)')
# plt.title(f'Reward Average per {window_size} Episodes with \n(epsilon ={epsilon}, epsilon_decrement={epsilon_dec}, Learning Rate={lr})')
# plt.legend()
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_bs_comparison/reward_avg_per_{window_size}_episodes_comparison_bs-{n_episodes}_eps_{epsilon}_eps_d_{epsilon_dec}_lr_{lr}.png'
# plt.savefig(plot_filename)
# print(f"Plot saved to: {plot_filename}")
# plt.show()
# ##############################
# ### rolling average
# plt.figure(figsize=(12, 6))
# for batch_size in batch_size_values:
# # Generate filename
# scores_filename = SCORES_FILENAME_TEMPLATE.format(
# episodes=n_episodes,
# eps=epsilon,
# eps_d=epsilon_dec,
# bs=batch_size,
# lr=lr
# )
# # Check if file exists
# if not os.path.isfile(scores_filename):
# print(f"File not found: {scores_filename}")
# continue
# # Load scores
# try:
# with open(scores_filename, 'r') as fp:
# scores = json.load(fp)
# except Exception as e:
# print(f"Error loading scores from {scores_filename}: {e}")
# continue
# # Calculate the rolling average of rewards
# rolling_avg_scores = np.convolve(scores, np.ones(rolling_window)/rolling_window, mode='valid')
# # Plot the rolling average
# plt.plot(range(rolling_window, len(scores) + 1), rolling_avg_scores, label=f'Batch Size={batch_size}', linestyle='--')
# # Finalize the plot
# plt.xlabel('Episode', fontsize=17)
# plt.ylabel(f'Rolling Average Reward (over {rolling_window} episodes)', fontsize=17)
# plt.title(f'Rolling Reward Average per {rolling_window} Episodes: Batch Size comparison with \n(epsilon ={epsilon}, epsilon_decrement={epsilon_dec}, Learning Rate={lr})', fontsize=17)
# plt.legend(fontsize=17)
# plt.grid(True)
# # Save and show the plot
# plot_filename = f'plots/ddqn/ddqn_bs_comparison/reward_rolling_avg_per_{rolling_window}_episodes_comparison_bs-{n_episodes}_eps_{epsilon}_eps_d_{epsilon_dec}_lr_{lr}.png'
# plt.savefig(plot_filename, bbox_inches='tight')
# print(f"Plot saved to: {plot_filename}")
# plt.show()
#########################################################
################# EPSILON DECREMENT #####################
#########################################################
# Prepare to plot
plt.figure(figsize=(12, 6))
for epsilon_dec in eps_dec_values:
# Generate filename
scores_filename = SCORES_FILENAME_TEMPLATE.format(
episodes=n_episodes,
eps=epsilon,
eps_d=epsilon_dec,
bs=batch_size,
lr=lr
)
# Check if file exists
if not os.path.isfile(scores_filename):
print(f"File not found: {scores_filename}")
continue
# Load scores
try:
with open(scores_filename, 'r') as fp:
scores = json.load(fp)
except Exception as e:
print(f"Error loading scores from {scores_filename}: {e}")
continue
# Plot
plt.plot(range(1, n_episodes + 1), scores, label=f'eps_dec={epsilon_dec}', marker='o')
# Finalize the plot
plt.xlabel('Episode')
plt.ylabel('Reward')
plt.title(f'Reward per Episode for different models with \n(epsilon ={epsilon}, Batch Size={batch_size}, Learning Rate={lr})')
plt.legend()
plt.grid(True)
# Save and show the plot
plot_filename = f'plots/ddqn/ddqn_eps_dec_comparison/reward_per_episode_comparison_eps_dec-{n_episodes}_eps_{epsilon}_bs_{batch_size}_lr_{lr}.png'
plt.savefig(plot_filename)
print(f"Plot saved to: {plot_filename}")
plt.show()
### Average reward plot
# Prepare to plot
plt.figure(figsize=(12, 6))
for epsilon_dec in eps_dec_values:
# Generate filename
scores_filename = SCORES_FILENAME_TEMPLATE.format(
episodes=n_episodes,
eps=epsilon,
eps_d=epsilon_dec,
bs=batch_size,
lr=lr
)
# Check if file exists
if not os.path.isfile(scores_filename):
print(f"File not found: {scores_filename}")
continue
# Load scores
try:
with open(scores_filename, 'r') as fp:
scores = json.load(fp)
except Exception as e:
print(f"Error loading scores from {scores_filename}: {e}")
continue
# Calculate the moving average for each window of 50 episodes
avg_scores = []
x_ticks = []
for i in range(0, len(scores), window_size):
window_avg = np.mean(scores[i:i + window_size])
avg_scores.append(window_avg)
x_ticks.append(i + window_size) # x values should be at the end of each window
# Plot
plt.plot(x_ticks, avg_scores, label=f'eps_dec={epsilon_dec}', marker='o')
# Finalize the plot
plt.xlabel('Episode')
plt.ylabel(f'Average Reward (over {window_size} episodes)')
plt.title(f'Reward Average per {window_size} Episodes with \n(epsilon ={epsilon}, Batch Size={batch_size}, Learning Rate={lr})')
plt.legend()
plt.grid(True)
# Save and show the plot
plot_filename = f'plots/ddqn/ddqn_eps_dec_comparison/reward_avg_per_{window_size}_episodes_comparison__eps_dec-{n_episodes}_eps_{epsilon}_bs_{batch_size}_lr_{lr}.png'
plt.savefig(plot_filename)
print(f"Plot saved to: {plot_filename}")
plt.show()
##############################
### rolling average
plt.figure(figsize=(12, 6))
plt.figure(figsize=(12, 6))
for epsilon_dec in eps_dec_values:
# Generate filename
scores_filename = SCORES_FILENAME_TEMPLATE.format(
episodes=n_episodes,
eps=epsilon,
eps_d=epsilon_dec,
bs=batch_size,
lr=lr
)
# Check if file exists
if not os.path.isfile(scores_filename):
print(f"File not found: {scores_filename}")
continue
# Load scores
try:
with open(scores_filename, 'r') as fp:
scores = json.load(fp)
except Exception as e:
print(f"Error loading scores from {scores_filename}: {e}")
continue
# Calculate the rolling average of rewards
rolling_avg_scores = np.convolve(scores, np.ones(rolling_window)/rolling_window, mode='valid')
# Plot the rolling average
plt.plot(range(rolling_window, len(scores) + 1), rolling_avg_scores, label=f'epsilon_dec={epsilon_dec}', linestyle='--')
# Finalize the plot
plt.xlabel('Episode', fontsize=17)
plt.ylabel(f'Rolling Average Reward (over {rolling_window} episodes)', fontsize=17)
plt.title(f'Rolling Reward Average per {rolling_window} Episodes: eps_dec comparison with \n(epsilon ={epsilon}, Batch Size={batch_size}, Learning Rate={lr})', fontsize=17)
plt.legend(fontsize=17)
plt.grid(True)
# Save and show the plot
plot_filename = f'plots/ddqn/ddqn_eps_dec_comparison/reward_rolling_avg_per_{rolling_window}_episodes_comparison_eps_dec-{n_episodes}_eps_{epsilon}_bs_{batch_size}_lr_{lr}.png'
plt.savefig(plot_filename, bbox_inches='tight')
print(f"Plot saved to: {plot_filename}")
plt.show()