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plots.py
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import json
from pathlib import Path
import numpy as np
from numpy.random import choice
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import statsmodels.api as sm
FIGURE_X = 6.0
FIGURE_Y = 4.0
STD_CURVE_COLOR = (0.88,0.70,0.678)
MEAN_CURVE_COLOR = (0.89,0.282,0.192)
SMOOTHING_CURVE_COLOR = (0.33,0.33,0.33)
CENTRALIZED_AGENT_COLOR = (0.2, 1.0, 0.2)
def globally_averaged_plot(centralized_J, distributed_J, results_path=None):
if results_path is None:
results_path = Path('data/results')
globally_averaged_return = np.array(distributed_J)
n_runs, n_steps = globally_averaged_return.shape
fig = plt.figure()
fig.set_size_inches(FIGURE_X, FIGURE_Y)
X = np.linspace(1, n_steps, n_steps)
Y = np.average(globally_averaged_return, axis=0)
lowess = sm.nonparametric.lowess(Y, X, frac=0.10)
plt.plot(X,Y, label=f'Mean', c=MEAN_CURVE_COLOR)
plt.plot(X,lowess[:,1], c=SMOOTHING_CURVE_COLOR, label='Smoothing')
plt.plot(X,np.mean(np.array(centralized_J), axis=0), c=CENTRALIZED_AGENT_COLOR, label='Centralized')
plt.xlabel('Time')
plt.ylabel('Globally Averaged Return J')
plt.legend(loc=4)
file_name = (results_path / 'globally_averaged_return.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'globally_averaged_return.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def advantages_plot(decentralized_A, results_path=None):
if results_path is None:
results_path = Path('data/results')
advantages = np.array(decentralized_A)
n_runs, n_steps, n_agents = advantages.shape
fig = plt.figure()
fig.set_size_inches(FIGURE_X, FIGURE_Y)
X = np.linspace(1, n_steps, n_steps)
Y = np.average(advantages, axis=0) # average number of runs
labels = [f'agent {_id}' for _id in range(n_agents)]
plt.plot(X,Y, label=labels)
lowess = sm.nonparametric.lowess(np.average(Y, axis=-1), X, frac=0.10)
plt.plot(X,lowess[:,1], c=SMOOTHING_CURVE_COLOR, label=f'smoothed')
plt.xlabel('Timesteps')
plt.ylabel('Advantages')
plt.legend(loc='upper right')
file_name = (results_path / 'advantages.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'advantages.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def delta_plot(centralized_deltas, decentralized_deltas, results_path=None):
if results_path is None:
results_path = Path('data/results')
deltas = np.array(decentralized_deltas)
n_runs, n_steps, n_agents = deltas.shape
fig = plt.figure()
fig.set_size_inches(FIGURE_X, FIGURE_Y)
X = np.linspace(1, n_steps, n_steps)
Y = np.average(deltas, axis=0) # average number of runs
T = np.average(np.array(centralized_deltas), axis=0)
labels = [f'agent {_id}' for _id in range(n_agents)]
plt.plot(X,Y, label=labels)
lowess = sm.nonparametric.lowess(np.average(Y, axis=-1), X, frac=0.10)
plt.plot(X,lowess[:,1], c=SMOOTHING_CURVE_COLOR, label=f'smoothed')
plt.plot(X, T, c=CENTRALIZED_AGENT_COLOR, label='Centralized')
plt.xlabel('Timesteps')
plt.ylabel('Deltas')
plt.legend(loc='upper right')
file_name = (results_path / 'deltas.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'deltas.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def mu_plot(centralized_mus, decentralized_mus, results_path=None):
if results_path is None:
results_path = Path('data/results')
mus = np.array(decentralized_mus)
n_runs, n_steps, n_agents = mus.shape
fig = plt.figure()
fig.set_size_inches(FIGURE_X, FIGURE_Y)
X = np.linspace(1, n_steps, n_steps)
Y = np.average(mus, axis=0) # average number of runs
T = np.average(np.array(centralized_mus), axis=0)
labels = [f'agent {_id}' for _id in range(n_agents)]
plt.plot(X,Y, label=labels)
lowess = sm.nonparametric.lowess(np.average(Y, axis=-1), X, frac=0.10)
plt.plot(X,np.average(Y, axis=-1), c=MEAN_CURVE_COLOR, label=f'Mean')
plt.plot(X,lowess[:,1], c=SMOOTHING_CURVE_COLOR, label=f'Smoothed')
plt.plot(X, T, c=CENTRALIZED_AGENT_COLOR, label='Centralized')
plt.xlabel('Timesteps')
plt.ylabel('Mus')
plt.legend(loc='upper right')
file_name = (results_path / 'mus.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'mus.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def pi_plot(centralized_pis, decentralized_pis, results_path=None):
# plt.rcParams['text.usetex'] = True
if results_path is None:
results_path = Path('data/results')
decentralized_pis = np.array(decentralized_pis)
n_runs, n_agents, n_probs = decentralized_pis.shape
decentralized_pis = np.average(decentralized_pis, axis=0).reshape(-1)
centralized_pis = np.array(centralized_pis)
n_runs, n_agents, n_probs = centralized_pis.shape
centralized_pis = np.average(centralized_pis, axis=0).reshape(-1)
width = 0.25 # the width of the bars
fig, ax = plt.subplots()
X = np.arange(1, n_agents * n_probs + 1, 1)
labels = [i if i % 5 == 0 else None for i in range(1, n_agents * n_probs + 1)]
rects1 = ax.bar(X - width/2, centralized_pis.tolist(), width, color=CENTRALIZED_AGENT_COLOR, label='Centralized')
rects2 = ax.bar(X + width/2, decentralized_pis.tolist(), width, color=MEAN_CURVE_COLOR, label='Distributed')
# Add some text for labels, title and custom x-axis tick labels, etc.
# ax.set_ylabel(r'$\displaystyle \text{Probabilities} \pi(s, a^i)')
ax.set_ylabel('Probabilities pi(s, a^i)')
ax.set_xlabel('(s, a^i)')
ax.set_title('s = 2')
ax.set_xticks(X)
ax.set_xticklabels(labels)
# ax.tick_params(axis='x', labelrotation=90)
ax.legend(loc='upper right')
fig.tight_layout()
file_name = (results_path / 'pis.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'pis.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def q_values_plot(centralized_Q, decentralized_Q, results_path=None):
if results_path is None:
results_path = Path('data/results')
q_values = np.array(decentralized_Q)
n_runs, n_steps, n_agents = q_values.shape
# draw tree agents
n_choice = min(n_agents, 3)
agent_ids = sorted(choice(n_agents, size=n_choice, replace=False).tolist())
fig = plt.figure()
fig.set_size_inches(FIGURE_X, FIGURE_Y)
X = np.linspace(1, n_steps, n_steps)
Y = np.average(q_values, axis=0) # average number of runs
Y = Y[:, agent_ids]
labels = [f'agent {agent_id}' for agent_id in agent_ids]
plt.plot(X,Y, label=labels)
labels = '-'.join([str(int(_id)) for _id in agent_ids])
lowess = sm.nonparametric.lowess(np.average(Y, axis=1), X, frac=0.10)
plt.plot(X,lowess[:,1], c=SMOOTHING_CURVE_COLOR, label=f'average {labels}')
plt.plot(X, np.average(centralized_Q, axis=0), c=CENTRALIZED_AGENT_COLOR, label='Centralized')
plt.xlabel('Timesteps')
plt.ylabel('Relative Q-values')
plt.legend(loc='upper right')
file_name = (results_path / 'q_values.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'q_values.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def log_plot(centralized_log, distributed_log, results_path=None):
if results_path is None:
results_path = Path('data/results')
# best actions plot
# same for centralized and decentralized
best_actions, best_actions_rewards = centralized_log['best_actions'], centralized_log['best_actions_rewards']
best_actions_plot(best_actions, results_path)
best_actions_rewards_plot(best_actions_rewards, results_path)
# states plot
centralized_states, distributed_states = centralized_log['state'], distributed_log['state']
states_plot(centralized_states, distributed_states, results_path)
# actions plot
centralized_actions, distributed_actions = centralized_log['actions'], distributed_log['actions']
actions_plot(centralized_actions, distributed_actions, results_path)
# rewards plot
centralized_average_rewards, distributed_average_rewards = centralized_log['reward'], distributed_log['reward']
average_rewards_plot(centralized_average_rewards, distributed_average_rewards, results_path)
def best_actions_plot(best_actions, results_path):
fig, ax = plt.subplots()
X = np.arange(len(best_actions))
ax.bar(X, best_actions)
ax.set_ylabel('action')
ax.set_xlabel('S')
ax.set_title('best action')
ax.set_xticks(X)
# ax.set_xticklabels(labels)
ax.legend(loc='upper right')
fig.tight_layout()
file_name = (results_path / 'best_actions.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'best_actions.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def best_actions_rewards_plot(best_actions_rewards, results_path):
fig, ax = plt.subplots()
X = np.arange(len(best_actions_rewards))
ax.bar(X, best_actions_rewards)
ax.set_ylabel('reward')
ax.set_xlabel('s')
ax.set_title('Best Team Reward')
ax.set_xticks(X)
ax.legend(loc='upper right')
fig.tight_layout()
file_name = (results_path / 'best_team_rewards.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'best_team_rewards.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def states_plot(centralized_states, distributed_states, results_path):
X = np.arange(len(distributed_states))
if results_path is None:
results_path = Path('data/results')
Y = np.array(distributed_states)
n_steps = Y.shape[0]
X = np.linspace(1, n_steps, n_steps)
smoothed_Y = sm.nonparametric.lowess(Y, X, frac=0.01)
T = np.array(centralized_states)
smoothed_T = sm.nonparametric.lowess(T, X, frac=0.01)
fig = plt.figure()
fig.set_size_inches(FIGURE_X, FIGURE_Y)
plt.plot(X, smoothed_Y[:, 1], label=f'Smoothed Distributed', c=MEAN_CURVE_COLOR)
plt.plot(X, smoothed_T[:, 1], label='Smoothed Centralized', c=CENTRALIZED_AGENT_COLOR)
plt.xlabel('Time')
plt.ylabel('States')
plt.legend(loc='upper right')
file_name = (results_path / 'states.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'states.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def actions_plot(centralized_actions, distributed_actions, results_path):
X = np.arange(len(distributed_actions))
if results_path is None:
results_path = Path('data/results')
Y = np.array(distributed_actions)
n_steps = Y.shape[0]
X = np.linspace(1, n_steps, n_steps)
smoothed_Y = sm.nonparametric.lowess(Y, X, frac=0.01)
T = np.array(centralized_actions)
smoothed_T = sm.nonparametric.lowess(T, X, frac=0.01)
fig = plt.figure()
fig.set_size_inches(FIGURE_X, FIGURE_Y)
X = np.linspace(1, n_steps, n_steps)
plt.plot(X, smoothed_Y[:, 1], label=f'Smoothed Distributed', c=MEAN_CURVE_COLOR)
plt.plot(X, smoothed_T[:, 1], label='Smoothed Centralized', c=CENTRALIZED_AGENT_COLOR)
plt.xlabel('Time')
plt.ylabel('Actions')
plt.legend(loc='upper right')
file_name = (results_path / 'actions.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'actions.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
def average_rewards_plot(centralized_average_rewards, distributed_average_rewards, results_path):
X = np.arange(len(distributed_average_rewards))
if results_path is None:
results_path = Path('data/results')
Y = np.cumsum(np.array(distributed_average_rewards) - 2)
T = np.cumsum(np.array(centralized_average_rewards) - 2)
n_steps = Y.shape[0]
fig = plt.figure()
fig.set_size_inches(FIGURE_X, FIGURE_Y)
X = np.linspace(1, n_steps, n_steps)
plt.plot(X, Y, label=f'Distributed', c=MEAN_CURVE_COLOR)
plt.plot(X, T, label='Centralized', c=CENTRALIZED_AGENT_COLOR)
plt.xlabel('Time')
plt.ylabel('Accumulated Team Rewards')
plt.legend(loc='upper right')
file_name = (results_path / 'average_rewards.pdf').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)
file_name = (results_path / 'average_rewards.png').as_posix()
plt.savefig(file_name, bbox_inches='tight', pad_inches=0)