def buildExperiment(alpha, gamma, lambdaa, importance):
net = nef.Network("HandWired parameters of RL node to bias")
net.add_to_nengo()
rl = rl_sarsa.qlambda("RL", noStateVars=2, noActions=4, noValues=20, logPeriod=2000)
world = gridworld.benchmarkA("map_20x20", "BenchmarkGridWorldNodeC", 10000)
net.add(rl) # place them into the network
net.add(world)
# connect them together
net.connect(world.getOrigin(QLambda.topicDataIn), rl.newTerminationFor(QLambda.topicDataIn))
net.connect(rl.getOrigin(QLambda.topicDataOut), world.getTermination(QLambda.topicDataOut))
# define the parameter sources (controllable from the simulation window)
net.make_input("alpha", [alpha])
net.make_input("gamma", [gamma])
net.make_input("lambda", [lambdaa])
net.make_input("importance", [importance])
# connect signal sources to the RL node
net.connect("alpha", rl.getTermination(QLambda.topicAlpha))
net.connect("gamma", rl.getTermination(QLambda.topicGamma))
net.connect("lambda", rl.getTermination(QLambda.topicLambda))
net.connect("importance", rl.getTermination(QLambda.topicImportance))
return net
def buildSimulation(alpha, gamma, lambdaa, importance,expName='test0'):
net=nef.Network('HandWired parameters of RL node to bias')
net.add_to_nengo()
#rl = rl_sarsa.qlambda("RL", noStateVars=2, noActions=4, noValues=20, logPeriod=2000)
rl = rl_sarsa.qlambdaASM("RL", noStateVars=2, noActions=4, noValues=20, logPeriod=2000)
world = gridworld.benchmarkA("map_20x20","BenchmarkGridWorldNodeC",10000);
net.add(rl) # place them into the network
net.add(world)
# connect them together
net.connect(world.getOrigin(QLambda.topicDataIn), rl.newTerminationFor(QLambda.topicDataIn))
net.connect(rl.getOrigin(QLambda.topicDataOut), world.getTermination(QLambda.topicDataOut))
# define the parameter sources (controllable from the simulation window)
net.make_input('alpha',[alpha])
net.make_input('gamma',[gamma])
net.make_input('lambda',[lambdaa])
net.make_input('importance',[importance])
# connect signal sources to the RL node
net.connect('alpha', rl.getTermination(QLambda.topicAlpha))
net.connect('gamma', rl.getTermination(QLambda.topicGamma))
net.connect('lambda', rl.getTermination(QLambda.topicLambda))
net.connect('importance', rl.getTermination(QLambda.topicImportance))
saver = net.add(ProsperitySaver('data_'+expName+'.txt'))
net.connect(rl.getOrigin(QLambda.topicProsperity),saver.getTermination("data"));
return net
def buildExperiment(alpha, gamma, lambdaa, importance):
net = nef.Network('HandWired parameters of RL node to bias')
net.add_to_nengo()
rl = rl_sarsa.qlambda("RL",
noStateVars=2,
noActions=4,
noValues=20,
logPeriod=2000)
world = gridworld.benchmarkA("map_20x20", "BenchmarkGridWorldNodeC", 10000)
net.add(rl) # place them into the network
net.add(world)
# connect them together
net.connect(world.getOrigin(QLambda.topicDataIn),
rl.newTerminationFor(QLambda.topicDataIn))
net.connect(rl.getOrigin(QLambda.topicDataOut),
world.getTermination(QLambda.topicDataOut))
# define the parameter sources (controllable from the simulation window)
net.make_input('alpha', [alpha])
net.make_input('gamma', [gamma])
net.make_input('lambda', [lambdaa])
net.make_input('importance', [importance])
# connect signal sources to the RL node
net.connect('alpha', rl.getTermination(QLambda.topicAlpha))
net.connect('gamma', rl.getTermination(QLambda.topicGamma))
net.connect('lambda', rl.getTermination(QLambda.topicLambda))
net.connect('importance', rl.getTermination(QLambda.topicImportance))
return net
def addModel(net, modelName='model0'):
"""
Add a new independent model into the network, return array of model components.
These can be then used for configuring the model.
"""
############################# define components
rl = rl_sarsa.qlambdaASM(modelName, noStateVars=2, noActions=4, noValues=15, logPeriod=2000, synchronous=False, classname="org.hanns.rl.discrete.ros.sarsa.config.QLambdaCoverageRewardFile")
world = gridworld.benchmarkA(modelName+"_map_15x15",mapName="BenchmarkGridWorldNodeD",logPeriod=100000);
source = motivation.basic(modelName, 1, Motivation.DEF_DECAY, logPeriod=10000)
net.add(rl) # place them into the network
net.add(world)
net.add(source)
################################ connect components together
# create tranform matrix which connects states dim of GridWorld (states) to the first dim of RL (dataIn=states)
# note: first dim is reward
tstates = [[0 for j in range(3)] for i in range(3)]
tstates[1][1] = 1;
tstates[2][2] = 1; # identity transform without first dimension (do not connect reward directly!)
#print tstates
net.connect(world.getOrigin(QLambda.topicDataIn), rl.newTerminationFor(QLambda.topicDataIn,tstates)) # world -> rl (states)
net.connect(rl.getOrigin(QLambda.topicDataOut), world.getTermination(QLambda.topicDataOut)) # rl -> world (actions)
# connect RL to the RL provided by/(passed through) the Motivaton source
treward = [[0 for j in range(1)] for i in range(3)] # yx (y is second dim)
treward[0][0] = 1;
net.connect(world.getOrigin(QLambda.topicDataIn), source.newTerminationFor(Motivation.topicDataIn,treward)) # world -> motivation (reward)
# create tranform matrix which connects first dim of Motivation (reward) to the first dim of RL (reward)
trr = [[0 for j in range(3)] for i in range(2)] # yx (y is second dim)
trr[0][0] = 1;
net.connect(source.getOrigin(Motivation.topicDataOut), rl.newTerminationFor(QLambda.topicDataIn,trr)) # motivation (r) -> rl (reward)
# connect Importance input of the RL to the Motivation provided by the Motivaton source
timportance = [[0 for j in range(1)] for i in range(2)] # yx
timportance[1][0] = 1;
#print timportance
net.connect(source.getOrigin(Motivation.topicDataOut), rl.newTerminationFor(QLambda.topicImportance,timportance)) # motivation -> rl (importance)
# connect GridWorld to the Importance input of RL module
tsi = [[0 for j in range(1)] for i in range(3)] # yx
#print tsi
net.connect(world.getOrigin(QLambda.topicDataIn), rl.newTerminationFor(QLambda.topicImportance, tsi)) # world -> rl (importance)
# connect to the decay port
net.make_input(modelName+'_decay',[0.01]) #net.make_input(name+'_decay',[Motivation.DEF_DECAY])
net.connect(modelName+'_decay', source.getTermination(Motivation.topicDecay))
########################## misc goes here
net.make_input(modelName+'_importance',[0])
net.connect(modelName+'_importance', rl.getTermination(QLambda.topicImportance))
saver = net.add(ProsperitySaver(modelName+'_saver.txt')) # TODO no need for name here?
net.connect(rl.getOrigin(QLambda.topicProsperity),saver.getTermination("RLprosperity"));
# return all new components
return [rl, world, source, saver, modelName, tstates, modelName]
def buildSimulation(alpha, gamma, lambdaa, importance, expName='test0'):
net = nef.Network('HandWired parameters of RL node to bias')
net.add_to_nengo()
rl = rl_sarsa.qlambdaASM(
"RL",
noStateVars=2,
noActions=4,
noValues=20,
logPeriod=2000,
classname="org.hanns.rl.discrete.ros.sarsa.QLambda",
prospLen=1)
world = gridworld.benchmarkA("map_20x20", "BenchmarkGridWorldNodeC", 10000)
net.add(rl) # place them into the network
net.add(world)
# connect them together
net.connect(world.getOrigin(QLambda.topicDataIn),
rl.newTerminationFor(QLambda.topicDataIn))
net.connect(rl.getOrigin(QLambda.topicDataOut),
world.getTermination(QLambda.topicDataOut))
# define the parameter sources (controllable from the simulation window)
net.make_input('alpha', [alpha])
net.make_input('gamma', [gamma])
net.make_input('lambda', [lambdaa])
net.make_input('importance', [importance])
# connect signal sources to the RL node
net.connect('alpha', rl.getTermination(QLambda.topicAlpha))
net.connect('gamma', rl.getTermination(QLambda.topicGamma))
net.connect('lambda', rl.getTermination(QLambda.topicLambda))
net.connect('importance', rl.getTermination(QLambda.topicImportance))
saver = net.add(ProsperitySaver('data_' + expName + '.txt'))
net.connect(rl.getOrigin(QLambda.topicProsperity),
saver.getTermination("data"))
return net
def buildSimulation(alpha, gamma, lambdaa, importance, expName="test0"):
net = nef.Network("HandWired parameters of RL node to bias")
net.add_to_nengo()
rl = rl_sarsa.qlambdaASM(
"RL",
noStateVars=2,
noActions=4,
noValues=20,
logPeriod=2000,
classname="org.hanns.rl.discrete.ros.sarsa.QLambda",
prospLen=1,
)
world = gridworld.benchmarkA("map_20x20", "BenchmarkGridWorldNodeC", 10000)
net.add(rl) # place them into the network
net.add(world)
# connect them together
net.connect(world.getOrigin(QLambda.topicDataIn), rl.newTerminationFor(QLambda.topicDataIn))
net.connect(rl.getOrigin(QLambda.topicDataOut), world.getTermination(QLambda.topicDataOut))
# define the parameter sources (controllable from the simulation window)
net.make_input("alpha", [alpha])
net.make_input("gamma", [gamma])
net.make_input("lambda", [lambdaa])
net.make_input("importance", [importance])
# connect signal sources to the RL node
net.connect("alpha", rl.getTermination(QLambda.topicAlpha))
net.connect("gamma", rl.getTermination(QLambda.topicGamma))
net.connect("lambda", rl.getTermination(QLambda.topicLambda))
net.connect("importance", rl.getTermination(QLambda.topicImportance))
saver = net.add(ProsperitySaver("data_" + expName + ".txt"))
net.connect(rl.getOrigin(QLambda.topicProsperity), saver.getTermination("data"))
return net
# Create the NeuralModule which implements discrete RL algorithm - Q(lambda) - Q-learning with eligibility traces # # Start the benchmark B # # by Jaroslav Vitku [[email protected]] import nef from ca.nengo.math.impl import FourierFunction from ca.nengo.model.impl import FunctionInput from ca.nengo.model import Units from ctu.nengoros.modules.impl import DefaultNeuralModule as NeuralModule from ctu.nengoros.comm.nodeFactory import NodeGroup as NodeGroup from ctu.nengoros.comm.rosutils import RosUtils as RosUtils from org.hanns.rl.discrete.ros.sarsa import QLambda import rl_sarsa import gridworld net=nef.Network('Demo of SARSA RL module interacting with the simulator of discrete 2D world with obstacles and rewards') net.add_to_nengo() rl = rl_sarsa.qlambdaASMConfigured("RL",net, noStateVars=2, noActions=4, noValues=20) # 2 state variables, 4 actions, xsize=20 world = gridworld.benchmarkA("map_20x20","BenchmarkGridWorldNodeC"); net.add(world) # data net.connect(world.getOrigin(QLambda.topicDataIn), rl.newTerminationFor(QLambda.topicDataIn)) net.connect(rl.getOrigin(QLambda.topicDataOut), world.getTermination(QLambda.topicDataOut)) print 'Configuration complete.'
# # Start the benchmark B # # by Jaroslav Vitku [[email protected]] import nef from ca.nengo.math.impl import FourierFunction from ca.nengo.model.impl import FunctionInput from ca.nengo.model import Units from ctu.nengoros.modules.impl import DefaultNeuralModule as NeuralModule from ctu.nengoros.comm.nodeFactory import NodeGroup as NodeGroup from ctu.nengoros.comm.rosutils import RosUtils as RosUtils from org.hanns.rl.discrete.ros.sarsa import QLambda import rl_sarsa import gridworld net=nef.Network('Demo of SARSA RL module interacting with the simulator of discrete 2D world with obstacles and rewards') net.add_to_nengo() rl = rl_sarsa.qlambdaASMConfigured("RL",net, noStateVars=2, noActions=4, noValues=30) # 2 state variables, 4 actions, xsize=30 # TODO this seems not to work now.. world = gridworld.benchmarkA("map_30x30","BenchmarkGridWorldNodeB"); net.add(world) # data net.connect(world.getOrigin(QLambda.topicDataIn), rl.newTerminationFor(QLambda.topicDataIn)) net.connect(rl.getOrigin(QLambda.topicDataOut), world.getTermination(QLambda.topicDataOut)) print 'Configuration complete.'
import nef
from ca.nengo.math.impl import FourierFunction
from ca.nengo.model.impl import FunctionInput
from ca.nengo.model import Units
from ctu.nengoros.modules.impl import DefaultNeuralModule as NeuralModule
from ctu.nengoros.comm.nodeFactory import NodeGroup as NodeGroup
from ctu.nengoros.comm.rosutils import RosUtils as RosUtils
from org.hanns.rl.discrete.ros.sarsa import QLambda
import rl_sarsa
import gridworld
net = nef.Network(
'Demo of SARSA RL module interacting with the simulator of discrete 2D world with obstacles and rewards'
)
net.add_to_nengo()
rl = rl_sarsa.qlambdaASMConfigured(
"RL", net, noStateVars=2, noActions=4,
noValues=20) # 2 state variables, 4 actions, xsize=20
world = gridworld.benchmarkA("map_20x20", "BenchmarkGridWorldNodeC")
net.add(world)
# data
net.connect(world.getOrigin(QLambda.topicDataIn),
rl.newTerminationFor(QLambda.topicDataIn))
net.connect(rl.getOrigin(QLambda.topicDataOut),
world.getTermination(QLambda.topicDataOut))
print 'Configuration complete.'