def run(arg):
task = arg[0]
parameters = arg[1]
#print "run with", parameters
seed = parameters["seed"]
process_id = hash(multiprocessing.current_process()._identity)
numpy.random.seed(seed + process_id)
render = False
plot = False
plt.ion()
env = CartPoleEnvironment()
if render:
renderer = CartPoleRenderer()
env.setRenderer(renderer)
renderer.start()
task_class = getattr(cp, task)
task = task_class(env, parameters["MaxRunsPerEpisode"])
testtask = task_class(env, parameters["MaxRunsPerEpisodeTest"],desiredValue=None)
#print "dim: ", task.indim, task.outdim
from pybrain.tools.shortcuts import buildNetwork
from pybrain.rl.agents import OptimizationAgent
from pybrain.optimization import PGPE
module = buildNetwork(task.outdim, task.indim, bias=False)
# create agent with controller and learner (and its options)
# % of random actions
#learner.explorer.epsilon = parameters["ExplorerEpsilon"]
agent = OptimizationAgent(module, PGPE(storeAllEvaluations = True,storeAllEvaluated=False, maxEvaluations=None,desiredEvaluation=1, verbose=False))
#
# print agent
# from pprint import pprint
# pprint (vars(agent.learner))
testagent = LearningAgent(module, None)
experiment = EpisodicExperiment(task, agent)
testexperiment = EpisodicExperiment(testtask, testagent)
def plotPerformance(values, fig):
plt.figure(fig.number)
plt.clf()
plt.plot(values, 'o-')
plt.gcf().canvas.draw()
# Without the next line, the pyplot plot won't actually show up.
plt.pause(0.001)
performance = []
if plot:
pf_fig = plt.figure()
m = parameters["MaxTotalEpisodes"]/parameters["EpisodesPerLearn"]
for episode in range(0,m):
# one learning step after one episode of world-interaction
experiment.doEpisodes(parameters["EpisodesPerLearn"])
#agent.learn(1)
#renderer.drawPlot()
# test performance (these real-world experiences are not used for training)
if plot:
env.delay = True
if (episode) % parameters["TestAfter"] == 0:
#print "Evaluating at episode: ", episode
#experiment.agent = testagent
#r = mean([sum(x) for x in testexperiment.doEpisodes(parameters["TestWith"])])
#for i in range(0,parameters["TestWith"]):
# y = testexperiment.doEpisodes(1)
# print (agent.learner._allEvaluated)
#
#
# from pprint import pprint
# pprint (vars(task))
l = parameters["TestWith"]
task.N = parameters["MaxRunsPerEpisodeTest"]
experiment.doEpisodes(l)
task.N = parameters["MaxRunsPerEpisode"]
resList = (agent.learner._allEvaluations)[-l:-1]
# print agent.learner._allEvaluations
from scipy import array
rLen = len(resList)
avReward = array(resList).sum()/rLen
# print avReward
# print resList
# exit(0)
# print("Parameters:", agent.learner._bestFound())
# print(
# " Evaluation:", episode,
# " BestReward:", agent.learner.bestEvaluation,
# " AverageReward:", avReward)
# if agent.learner.bestEvaluation == 0:
#
# print resList[-20:-1]
# print "done"
# break
performance.append(avReward)
env.delay = False
testagent.reset()
#experiment.agent = agent
# performance.append(r)
if plot:
plotPerformance(performance, pf_fig)
# print "reward avg", r
# print "explorer epsilon", learner.explorer.epsilon
# print "num episodes", agent.history.getNumSequences()
# print "update step", len(performance)
# print "done"
return performance
#print "network", json.dumps(module.bn.net.E, indent=2)
#import sumatra.parameters as p
#import sys
#parameter_file = sys.argv[1]
#parameters = p.SimpleParameterSet(parameter_file)
#
#
#run(["BalanceTask",parameters])
from pybrain.rl.experiments import EpisodicExperiment
from pybrain.rl.learners.valuebased import NFQ, ActionValueNetwork
from pybrain.rl.explorers import BoltzmannExplorer
from numpy import array, arange, meshgrid, pi, zeros, mean
from matplotlib import pyplot as plt
# switch this to True if you want to see the cart balancing the pole (slower)
render = False
plt.ion()
env = CartPoleEnvironment()
if render:
renderer = CartPoleRenderer()
env.setRenderer(renderer)
renderer.start()
module = ActionValueNetwork(4, 3)
task = DiscreteBalanceTask(env, 100)
learner = NFQ()
learner.explorer.epsilon = 0.4
agent = LearningAgent(module, learner)
testagent = LearningAgent(module, None)
experiment = EpisodicExperiment(task, agent)
def plotPerformance(values, fig):
plt.figure(fig.number)
plt.clf()
plt.plot(values, 'o-')
def run(arg):
task = arg[0]
parameters = arg[1]
#print "run with", parameters
seed = parameters["seed"]
process_id = hash(multiprocessing.current_process()._identity)
numpy.random.seed(seed + process_id)
render = False
plot = False
plt.ion()
env = CartPoleEnvironment()
if render:
renderer = CartPoleRenderer()
env.setRenderer(renderer)
renderer.start()
task_class = getattr(cp, task)
task = task_class(env, parameters["MaxRunsPerEpisode"])
testtask = task_class(env, parameters["MaxRunsPerEpisodeTest"])
#print "dim: ", task.indim, task.outdim
# to inputs state and 4 actions
module = ActionValueNetwork(task.outdim, task.indim)
learner = NFQ()
# % of random actions
learner.explorer.epsilon = parameters["ExplorerEpsilon"]
agent = LearningAgent(module, learner)
testagent = LearningAgent(module, None)
experiment = EpisodicExperiment(task, agent)
testexperiment = EpisodicExperiment(testtask, testagent)
def plotPerformance(values, fig):
plt.figure(fig.number)
plt.clf()
plt.plot(values, 'o-')
plt.gcf().canvas.draw()
# Without the next line, the pyplot plot won't actually show up.
plt.pause(0.001)
performance = []
if plot:
pf_fig = plt.figure()
m = parameters["MaxTotalEpisodes"]/parameters["EpisodesPerLearn"]
for episode in range(0,m):
# one learning step after one episode of world-interaction
experiment.doEpisodes(parameters["EpisodesPerLearn"])
agent.learn(1)
#renderer.drawPlot()
# test performance (these real-world experiences are not used for training)
if plot:
env.delay = True
if (episode) % parameters["TestAfter"] == 0:
#print "Evaluating at episode: ", episode
#experiment.agent = testagent
r = mean([sum(x) for x in testexperiment.doEpisodes(parameters["TestWith"])])
env.delay = False
testagent.reset()
#experiment.agent = agent
performance.append(r)
if plot:
plotPerformance(performance, pf_fig)
# print "reward avg", r
# print "explorer epsilon", learner.explorer.epsilon
# print "num episodes", agent.history.getNumSequences()
# print "update step", len(performance)
# print "done"
return performance
#print "network", json.dumps(module.bn.net.E, indent=2)
from pybrain.rl.explorers import BoltzmannExplorer
from numpy import array, arange, meshgrid, pi, zeros, mean
from matplotlib import pyplot as plt
# switch this to True if you want to see the cart balancing the pole (slower)
render = False
#render = True
plt.ion()
env = CartPoleEnvironment()
if render:
renderer = CartPoleRenderer()
env.setRenderer(renderer)
renderer.start()
module = ActionValueNetwork(4, 3)
task = DiscreteBalanceTask(env, 100)
learner = NFQ()
learner.explorer.epsilon = 0.4
agent = LearningAgent(module, learner)
testagent = LearningAgent(module, None)
experiment = EpisodicExperiment(task, agent)
def plotPerformance(values, fig):
plt.figure(fig.number)
plt.clf()
def run(arg):
task = arg[0]
parameters = arg[1]
#print "run with", task,parameters
seed = parameters["seed"]
process_id = hash(multiprocessing.current_process()._identity)
numpy.random.seed(seed)
render = False
plot = False
plt.ion()
env = CartPoleEnvironment()
env.randomInitialization = False
if render:
renderer = CartPoleRenderer()
env.setRenderer(renderer)
renderer.start()
task_class = getattr(cp, task)
task = task_class(env, 50)
#print "dim: ", task.indim, task.outdim
# to inputs state and 4 actions
bmodule = ActionValueRAND(task.outdim, task.indim)
rlearner = RAND()
blearner = RAND()
# % of random actions
bagent = LearningAgent(bmodule, rlearner)
from pybrain.tools.shortcuts import buildNetwork
from pybrain.rl.agents import OptimizationAgent
from pybrain.optimization import PGPE
module = buildNetwork(task.outdim, task.indim, bias=False)
# create agent with controller and learner (and its options)
# % of random actions
#learner.explorer.epsilon = parameters["ExplorerEpsilon"]
agent = OptimizationAgent(module, PGPE(storeAllEvaluations = True,storeAllEvaluated=True, maxEvaluations=None, verbose=False))
testagent = LearningAgent(module, None)
pgpeexperiment = EpisodicExperiment(task, agent)
randexperiment = EpisodicExperiment(task, bagent)
def plotPerformance(values, fig):
plt.figure(fig.number)
plt.clf()
plt.plot(values, 'o-')
plt.gcf().canvas.draw()
# Without the next line, the pyplot plot won't actually show up.
plt.pause(0.001)
performance = []
if plot:
pf_fig = plt.figure()
m = parameters["MaxTotalEpisodes"]/parameters["EpisodesPerLearn"]
## train pgpe
for episode in range(0,50):
# one learning step after one episode of world-interaction
y =pgpeexperiment.doEpisodes(1)
be, bf = agent.learner._bestFound()
print be,bf
print "generate data"
be.numActions = 1
gdagent = LearningAgent(be, blearner)
experiment = EpisodicExperiment(task, gdagent)
for episode in range(0,1000):
# print episode, " of 1000"
# one learning step after one episode of world-interaction
y =experiment.doEpisodes(1)
# print y
x = randexperiment.doEpisodes(1)
# print len(y[0])
#renderer.drawPlot()
# test performance (these real-world experiences are not used for training)
if plot:
env.delay = True
l = 5
resList = (agent.learner._allEvaluations)[-l:-1]
# print agent.learner._allEvaluations
from scipy import array
rLen = len(resList)
avReward = array(resList).sum()/rLen
# print avReward
# print resList
# exit(0)
# print("Parameters:", agent.learner._bestFound())
# print(
# " Evaluation:", episode,
# " BestReward:", agent.learner.bestEvaluation,
# " AverageReward:", avReward)
# if agent.learner.bestEvaluation == 0:
#
# print resList[-20:-1]
# print "done"
# break
#print resList
performance.append(avReward)
env.delay = False
testagent.reset()
#experiment.agent = agent
# performance.append(r)
if plot:
plotPerformance(performance, pf_fig)
# print "reward avg", r
# print "explorer epsilon", learner.explorer.epsilon
# print "num episodes", agent.history.getNumSequences()
# print "update step", len(performance)
blearner.add_ds(rlearner.dataset)
blearner.learn()
#blearner.learnX(agent.learner._allEvaluated)
print "done"
return performance
