def __init__(self):
self.env = MountainCar()
self.noiseRange = 1.0
self.om = 0
self.alpha = 0.6
self.beta = 0.4
self.t = 0
self.totStep = 0
self.r = 0
self.ep = 0
self.perfs = result_log(algo="DDPG", l1=20, l2=10)
self.actif = True
def __init__(self, logger = None):
self.env = MountainCar()
self.noiseRange = 1.0
self.noiseMax = 1.0
self.om = 0
self.alpha = 0.6
self.beta = 0.4
self.t = 0
self.totStep = 0
self.r = 0
self.ep = 0
if logger==None:
self.perfs = result_log(algo="DDPG", l1=20, l2=10)
else:
self.perfs = logger
self.actif = True
if CMA_obj.totStep != term_callback.lastCall:
term_callback.lastCall = CMA_obj.totStep
CMA_obj.act.load_parameters(c.mean)
CMA_obj.env.reset(noise = False)
ret = 0
while not CMA_obj.env.isFinished():
actn, r = CMA_obj.env.act(CMA_obj.act.action_batch(CMA_obj.env.state()))
ret += r[0]
term_callback.plot_data.addData(CMA_obj.totStep, CMA_obj.env.t, ret)
return False
CMA_obj.env = MountainCarEnv()
CMA_obj.act = simple_actor_network(2, 1, l1_size = 20, l2_size = 10)
CMA_obj.totStep = 0
term_callback.plot_data = result_log("CMA-ES", 20,10)
term_callback.lastCall = -1
print "Going for CMA-ES"
op = cma.CMAOptions()
op['termination_callback'] = term_callback
op['maxiter']=200
x= CMA_obj.act.linear_parameters()
fx = cma.fmin(CMA_obj, x, 0.5, options = op)
term_callback.plot_data.save()
def draw_politic():
plt.close()
act = CMA_obj.act
img = np.zeros((200, 200))
pos = -1.
CMA_obj.act.load_parameters(c.mean)
CMA_obj.env.reset(noise=False)
ret = 0
while not CMA_obj.env.isFinished():
actn, r = CMA_obj.env.act(
CMA_obj.act.action_batch(CMA_obj.env.state()))
ret += r[0]
term_callback.plot_data.addData(CMA_obj.totStep, CMA_obj.env.t, ret)
return False
CMA_obj.env = MountainCarEnv()
CMA_obj.act = simple_actor_network(2, 1, l1_size=20, l2_size=10)
CMA_obj.totStep = 0
term_callback.plot_data = result_log("CMA-ES", 20, 10)
term_callback.lastCall = -1
print "Going for CMA-ES"
op = cma.CMAOptions()
op['termination_callback'] = term_callback
op['maxiter'] = 200
x = CMA_obj.act.linear_parameters()
fx = cma.fmin(CMA_obj, x, 0.5, options=op)
term_callback.plot_data.save()
def draw_politic():
plt.close()
act = CMA_obj.act
img = np.zeros((200, 200))
@author: arnaud
"""
from DDPG.core.DDPG_core import DDPG
import numpy as np
import DDPG.environement.instance.mountainCarEnv as mc
from DDPG.core.networks.simple_actor_network import simple_actor_network
from DDPG.logger.result import result_log
l1 = 20
l2 = 10
rate = 0.001
env = mc.MountainCarEnv(result_log("DDPG", l1, l2))
a_c = DDPG(env,
actor=simple_actor_network(2,
1,
l1_size=l1,
l2_size=l2,
learning_rate=rate))
def voidFunc():
pass
env.extern_draw = voidFunc
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 21 13:31:15 2016
@author: arnaud
"""
from DDPG.core.DDPG_core import DDPG
import numpy as np
import DDPG.environement.instance.mountainCarEnv as mc
from DDPG.core.networks.simple_actor_network import simple_actor_network
from DDPG.logger.result import result_log
l1 = 20
l2 = 10
rate = 0.001
env = mc.MountainCarEnv(result_log("DDPG", l1, l2))
a_c = DDPG(env, actor = simple_actor_network(2, 1, l1_size = l1, l2_size = l2, learning_rate = rate))
def voidFunc():
pass
env.extern_draw = voidFunc
def doEp(M, T=float("inf")):
a_c.M_episodes(M, T)
env.perfs.save()
doEp(4000)
"""
from DDPG.core.DDPG_core import DDPG
import numpy as np
import DDPG.environement.instance.mountainCarEnv as mc
from DDPG.core.networks.simple_actor_network import simple_actor_network
from DDPG.core.networks.simple_critic_network import simple_critic_network
import matplotlib.pyplot as plt
from DDPG.logger.result import result_log
l1 = 20
l2 = 10
logger = result_log("DDPG", l1, l2, "simple_"+str(l1)+"_"+str(l2))
env = mc.MountainCarEnv(logger)
a_c = DDPG(env, actor = simple_actor_network(2, 1, l1_size = l1, l2_size = l2, learning_rate = 0.005), critic = simple_critic_network(2, 1, l1_size = 20, l2_size = 10, learning_rate = 0.01))
def draw_politic():
plt.close()
ac= a_c
img = np.zeros((200, 200))
pos = -1.
batch = []
for i in range(200):
vel = -1.
pos += 0.01
for j in range(200):
vel += 0.01
