def plot_tactical():
tactical_data = np.genfromtxt(
'/home/jazz/Documents/openai-results/open-ai-tactical.csv',
delimiter=',')
plt.plot([i[0] for i in tactical_data],
pu.smooth([i[1] for i in tactical_data], radius=1),
linewidth=1,
linestyle=lines[0],
color=colors[0])
plt.xlabel('Number of steps')
plt.ylabel('Reward per episode')
plt.show()
def main(logs='logs-arm', n_arm=3, smooth=True):
logs = glob.glob(join(logs, str(n_arm),'*'))
for log in logs:
results = pu.load_results(log)
r = results[0]
eps = np.cumsum(r[1].epoch).values
dist = r[1]['test/success_rate'].values
if smooth:
dist = pu.smooth(dist, radius=2)
plt.plot(eps, dist, label=basename(log))
plt.legend()
plt.title('Number of arms: {}'.format(n_arm))
plt.show()
def plot_training(experiment_no, experiment_dir):
results = plot_util.load_results(experiment_dir, verbose=True)
results.sort(key=lambda x: x.dirname)
fig, ax = plt.subplots()
color_set = ['b', 'g', 'r', 'm', 'y', 'c']
ax.grid(color='#dddddd', linestyle='-', linewidth=1)
for i, r in enumerate(results):
i_ = i % len(color_set)
plt.plot(np.cumsum(r.monitor.l), plot_util.smooth(r.monitor.r, radius=200), color=color_set[i_])
for i, r in enumerate(results):
i_ = i % len(color_set)
plt.plot(np.cumsum(r.monitor.l), r.monitor.r, alpha=0.1, color=color_set[i_])
ax.ticklabel_format(axis='x', style='sci', scilimits=(0, 0))
ax.set_xlim(left=0)
plt.tight_layout(pad=2)
plt.xlabel('time steps')
plt.ylabel('episode_reward')
plt.title('experiment_{}'.format(experiment_no), fontsize=10)
plt.legend([os.path.basename(r.dirname) for r in results])
plt.savefig(os.path.join(experiment_dir, 'training_{}.png'.format(experiment_no)))
plt.show()
def individual_graph():
for i in range(len(results)):
# print(np.ones(3))
# print(np.cumsum(r.monitor.l))
# print(r.monitor.r)
# print(r.monitor.r.mean())
# print(pu.smooth(r.monitor.r, radius=8))
# for l in np.cumsum(r.monitor.l):
# plt.axvline(l, linestyle=':', linewidth=1)
# plt.plot(np.cumsum(r.monitor.l), r.monitor.r)
# Understanding convolution with window size: https://stackoverflow.com/a/20036959/7308982
plt.plot(np.cumsum(results[i].monitor.l),
pu.smooth(results[i].monitor.r, radius=1),
linewidth=1,
linestyle=lines[i],
color=colors[i])
plt.xlim(right=500)
plt.xlabel('Number of steps')
plt.ylabel('Average reward per episode')
plt.show()
def average_graph():
min_number_episodes = 10000
for i in range(len(results)):
result = results[i]
rewards = result.monitor.r
number_episodes = len(rewards)
print('Results {}: number of episodes = {}'.format(i, number_episodes))
if (number_episodes < min_number_episodes):
min_number_episodes = number_episodes
total_reward_per_episode = [0] * min_number_episodes
for i in range(len(results)):
result = results[i]
rewards = result.monitor.r
for j in range(min_number_episodes):
reward_episode = rewards[j]
total_reward_per_episode[j] += reward_episode
average_reward_per_episode = [
x / len(results) for x in total_reward_per_episode
]
print(min_number_episodes)
plt.plot(range(len(total_reward_per_episode)),
pu.smooth(average_reward_per_episode, radius=1),
linewidth=1,
linestyle=lines[0],
color=colors[0])
# plt.plot(range(len(total_reward_per_episode)), average_reward_per_episode, linewidth=1, linestyle=lines[0], marker=markers[0], markersize=3, color=colors[0])
plt.xlabel('Number of episodes')
plt.ylabel('Average reward per episode')
plt.show()
def tr_fn(r):
x = r.monitor.t
y = smooth(r.monitor.r, radius=10)
return x, y
def lr_fn(r):
x = np.cumsum(r.monitor.l)
y = smooth(r.monitor.r, radius=10)
return x, y
def xy_fn(r):
x = np.divide(r.progress.steps, 1000)
y = smooth(r.progress["mean 100 episode reward"], radius=1)
#x = np.divide(r.progress.total_timesteps, 1000)
#y = smooth(r.progress.eprewmean, radius=1)
return x, y
from baselines.common import plot_util as pu
mujoco_results = pu.load_results(
'/home/developer/logs/her_pyrobot_push_mujoco/250k_v2')
#results = pu.load_results('/home/developer/logs/her_pyrobot_reach/joint_100k_v4')
import matplotlib.pyplot as plt
import numpy as np
mujoco_r = mujoco_results[0]
mujoco_arr = np.array(mujoco_r.progress)
epoch = mujoco_arr[:, 0]
mujoco_test_success_rate = mujoco_arr[:, 7]
mujoco_train_success_rate = mujoco_arr[:, 9]
plt.suptitle(
'LocoBot Push Task Trained in MuJoCo with 250k timesteps(100 epoches)~')
plt.title('Training in Mujoco')
plt.plot(epoch,
pu.smooth(mujoco_train_success_rate, radius=10),
label="mujoco_train")
plt.plot(epoch,
pu.smooth(mujoco_test_success_rate, radius=10),
label="mujoco_test")
plt.xlabel('epoches')
plt.ylabel('success_rate')
plt.legend()
#plt.plot(np.cumsum(r.monitor.l), pu.smooth(r.monitor.r, radius=10))
plt.show()
model.save('berater-ppo-v8.pkl')
monitored_env.close()
##################################################
print("--- Plot ---")
from baselines.common import plot_util as pu
results = pu.load_results(log_dir)
import matplotlib.pyplot as plt
import numpy as np
r = results[0]
plt.ylim(0, .75)
# plt.plot(np.cumsum(r.monitor.l), r.monitor.r)
plt.plot(np.cumsum(r.monitor.l), pu.smooth(r.monitor.r, radius=100))
##################################################
import numpy as np
print("--- Enjoy ---")
BeraterEnv.BeraterEnv.showStep = True
for c in range(1000):
observation = env.reset()
for c in range(1000):
actions, _, state, _ = model.step(observation)
from baselines.common import plot_util as pu
import matplotlib.pyplot as plt
import numpy as np
num = input()
results = pu.load_results('logs/single_ppo_092901')
r = results[0]
print(r.monitor.r)
plt.plot(np.cumsum(r.monitor['l']), pu.smooth(r.monitor['r'], radius=10))
plt.show()
# # plt.plot(r.progress.total_timesteps, r.progress.eprewmean)
import json
import numpy as np
import matplotlib.pyplot as plt
from baselines.common import plot_util as pu
exp_id = 'payload_empty_her100_demo'
results = pu.load_results('../policies/{}'.format(exp_id))[0]
epoch = results.progress["epoch"]
trainscs = results.progress["train/success_rate"]
testscs = results.progress["test/success_rate"]
plt.plot(epoch, pu.smooth(trainscs, radius=1), label="Train")
plt.plot(epoch, pu.smooth(testscs, radius=1), label="Test")
# plt.plot(epoch,trainscs,label="Train")
# plt.plot(epoch,testscs,label="Test")
plt.xlabel('Epoch')
plt.ylabel('Success Rate')
f = open('../policies/{}/params.json'.format(exp_id))
params = json.load(f)
plt.title(params['env_name'])
plt.legend()
#plt.show()
#print(results.progress)
plt.savefig('plots/exp_{}_results.png'.format(exp_id))
def plot():
results = pu.read_csv('results/baselines/ppo_50m.monitor.csv')
plt.plot(np.cumsum(results.l), pu.smooth(results.r, radius=100))
plt.show()
from baselines.common import plot_util as plot
import matplotlib.pyplot as plt
import numpy as np
results = plot.load_results('~/logs/HER')
print(results)
r = results[0]
plt.plot(np.cumsum(r.monitor.l), plot.smooth(r.monitor.r,
radius=10)) # radius to smoother
plt.xlabel('Iterations')
plt.ylabel('Reward')
plt.show()
