init_angle_velocities = np.array([0., 0., 0.]) # initial angle velocities
file_output = 'data.txt' # file name for saved results
num_episodes = 1000
target_pos = np.array([5., 5., 5.])
task = Task(init_pose, init_velocities, init_angle_velocities, runtime,
target_pos)
agent = PolicySearch_Agent(task)
rewards = []
for i_episode in range(1, num_episodes + 1):
state = agent.reset_episode() # start a new episode
while True:
action = agent.act(state)
next_state, reward, done = task.step(action)
agent.step(reward, done)
state = next_state
if done:
rewards.append(agent.total_reward)
print(
"\rEpisode = {:4d}, score = {:7.3f} (best = {:7.3f}), noise_scale = {}"
.format(i_episode, agent.total_reward, agent.best_reward,
agent.noise_scale),
end="") # [debug]
break
sys.stdout.flush()
plt.plot(rewards)
plt.legend('rewards')
_ = plt.ylim()
plt.show()
import sys
from agents.policy_search import PolicySearch_Agent
from task import Task
import numpy as np
num_episodes = 1000
target_pos = np.array([0., 0., 10.])
task = Task(target_pos=target_pos)
agent = PolicySearch_Agent(task)
for i_episode in range(1, num_episodes + 1):
state = agent.reset_episode()
while True:
action = agent.act(state)
next_state, reward, done = task.step(action)
agent.step(reward, done, next_state, done)
state = next_state
if done:
print(
"\rEpisode = {:4d}, score = {:7.3f} (best = {:7.3f}), noise_scale = {}"
.format(i_episode, agent.score, agent.best_score,
agent.noise_scale),
end="") # [debug]
break
sys.stdout.flush()