# ------
r = exp.doEpisodes(1)
# Discounted reward.
cumreward = exp.task.getTotalReward()
#print 'cumreward: %.4f; nsteps: %i; learningRate: %.4f' % (
# cumreward, len(r[0]), exp.agent.learner.learningRate)
if irehearsal % 50 == 0:
# Perform (no learning).
# ----------------------
# Swap out the agent.
exp.agent = performance_agent
# Perform.
r = exp.doEpisodes(1)
perform_cumreward = task.getTotalReward()
perform_cumrewards.append(perform_cumreward)
print('PERFORMANCE: cumreward:', perform_cumreward, 'nsteps:',
len(r[0]))
# Swap back the learning agent.
performance_agent.reset()
exp.agent = agent
ax1.cla()
ax1.plot(perform_cumrewards, '.--')
# Wheel trajectories.
update_wheel_trajectories()
plt.pause(0.001)
# Learn.
# ------
r = exp.doEpisodes(1)
# Discounted reward.
cumreward = exp.task.getTotalReward()
# print 'cumreward: %.4f; nsteps: %i; learningRate: %.4f' % (
# cumreward, len(r[0]), exp.agent.learner.learningRate)
if irehearsal % 50 == 0:
# Perform (no learning).
# ----------------------
# Swap out the agent.
exp.agent = performance_agent
# Perform.
r = exp.doEpisodes(1)
perform_cumreward = task.getTotalReward()
perform_cumrewards.append(perform_cumreward)
print "PERFORMANCE: cumreward:", perform_cumreward, "nsteps:", len(r[0])
# Swap back the learning agent.
performance_agent.reset()
exp.agent = agent
ax1.cla()
ax1.plot(perform_cumrewards, ".--")
# Wheel trajectories.
update_wheel_trajectories()
plt.pause(0.001)
for iteration in range(100000):
#print("ITERATION : " , iteration)
r = exp.doEpisodes(1)
cumreward = exp.task.getTotalReward()
#print 'cumreward: %.4f; nsteps: %i; learningRate: %.4f' % (cumreward, len(r[0]), exp.agent.learner.learningRate)
if iteration % 15 == 0:
exp.agent = max_agent
r = exp.doEpisodes(1)
perform_cumreward = task.getTotalReward()
perform_cumrewards.append(perform_cumreward)
print('PERFORMANCE: cumreward:', perform_cumreward, 'nsteps:',
len(r[0]))
stats = (task.env.get_yfhist())
new_stats = [
np.max(stats),
np.mean(stats),
np.median(stats), stats[-1], perform_cumreward, iteration,
exp.agent.learner.learningRate
]
new_stats = [str(s) for s in new_stats]
with open("res/lspi_30-50.txt", "a") as myfile:
myfile.write(" ".join(new_stats) + "\n")
max_agent.reset()
exp.agent = agent
ax1.cla()
ax1.plot(perform_cumrewards, '.--')
# Wheel trajectories.
update_wheel_trajectories()
plt.pause(0.001)
