def makeCPTpure(self, setCPT=True):
"""Convert mixed CPT to pure CPT,
:arg setCPT: if True (default), then the CPT attribute is converted to
a pure CPT. Otherwise, the output is a pure CPT.
:type setCPT: bool
.. note::
whenever there are multiple argmax's, each gets equal probability in
the resuling "pure" CPT.
"""
if setCPT:
self.CPT = convert_2_pureCPT(self.CPT)
else:
return convert_2_pureCPT(copy.copy(self.CPT))
def makeCPTpure(self, setCPT=True):
"""Convert mixed CPT to pure CPT,
:arg setCPT: if True (default), then the CPT attribute is converted to
a pure CPT. Otherwise, the output is a pure CPT.
:type setCPT: bool
.. note::
whenever there are multiple argmax's, each gets equal probability in
the resuling "pure" CPT.
"""
if setCPT:
self.CPT = convert_2_pureCPT(self.CPT)
else:
return convert_2_pureCPT(copy.copy(self.CPT))
def train_node(self, nodename, level, logit=False, setCPT=False, verbose=False):
"""Compute level-k best response at the DN given Game
:arg nodename: the name of the decision node where MCEUs are estimated
:type nodename: str
:arg level: The level at which to train that player
:type level: int
:arg setCPT: If the trained CPT should be set as the current CPT.
Otherwise, it can be accessed through node.LevelCPT. Default is
False
:type setCPT: bool
Notes
-----
If training a player at level k, the other players' CPT will be accessed
through self.Game.node_dict[other_player].LevelCPT[k-1]
"""
print 'Training ' + nodename + ' at level ' + str(level)
Game = copy.deepcopy(self.Game) # copy in order to maintain original CPT
ps = self.specs
for node in Game.node_dict.values(): # Game changes, self.Game doesn't
if type(node) is pynfg.DecisionNode:
try:
node.CPT = node.LevelCPT[level - 1]
except KeyError:
raise KeyError('Need to train other players at level %s'
% str(level-1))
EUtable = mceu(Game, nodename, Game.node_dict[nodename].N,
Game.node_dict[nodename].tol, Game.node_dict[nodename].delta,
verbose=verbose)
if not logit:
self.Game.node_dict[nodename].LevelCPT[level] = \
convert_2_pureCPT(EUtable)
if setCPT:
self.Game.node_dict[nodename].CPT = convert_2_pureCPT(EUtable)
else:
weight = np.exp(Game.node_dict[nodename].beta*EUtable)
norm = np.sum(weight, axis=-1)
self.Game.node_dict[nodename].LevelCPT[level] = \
weight/norm[..., np.newaxis]
if setCPT:
self.Game.node_dict[nodename].CPT = weight/norm[..., np.newaxis]
def bestresponse_node(Game, dn, N, delta=1, tol=30, verbose=False):
"""Compute level-k best response at the DN given Game
:arg Game: the Network Form Game of interest
:type Game: SemiNFG or iterSemiNFG
:arg dn: the name of the decision node where MCEUs are estimated
:type dn: str
:arg N: the max number of iterations for the estimation
:type N: int
:arg tol: the minimum number of samples per parent value
:type tol: int
"""
G = copy.deepcopy(Game)
EUtable = mceu(G, dn, N, tol, delta, verbose)
G.node_dict[dn].CPT = convert_2_pureCPT(EUtable)
return G
def train_node(self, bn, level, setCPT=False):
"""Solve for the optimal policy using Optimistic Q-learning. Optimistic
Q-Learning is an off-policy TD control RL algorithm
:arg bn: The basename of the node with the CPT to be trained
:type bn: str
:arg level: The level at which to train the basename
:type level: int
"""
print 'Training ' + bn + ' at level '+ str(level)
Game = copy.deepcopy(self.Game)
ps = self.specs
player = Game.bn_part[bn][0].player
w, d, N, r_max = ps[player]['w'], ps[player]['delta'], ps[player][bn]['N'], \
ps[player][bn]['r_max']
#Set other CPTs to level-1. Works even if CPTs aren't pointers.
for o_player in Game.players:
bn_list = list(set(map(lambda x: x.basename, Game.partition[o_player])))
for base in bn_list:
if base != bn:
for dn in Game.bn_part[base]:
try:
dn.CPT = \
(self.trained_CPTs[o_player][base][level - 1])
except KeyError:
raise KeyError('Need to train other players at level %s'
% str(level-1))
T0 = Game.starttime #get the start time
T = Game.endtime + 1 #get the end time
shape = Game.bn_part[bn][T0].CPT.shape #the shape of CPT
if d<1:
Q0 = r_max*((1-d**(T-T0))/(1-d)) #the initial q value
else:
Q0 = r_max*(T-T0)
Q = Q0 * np.ones(shape) #the initial q table
visit = np.zeros(shape)
#the number of times each (m,a) pair has been visited.
r_av = 0 #the dynamic (discounted) average reward
rseries = [] #a series of average rewards
for ep in xrange(N):
print ep
#convert Q table to CPT
Game.bn_part[bn][T0].CPT = convert_2_pureCPT(Q)
Game.sample_timesteps(T0,T0) #sample the start time step
malist = Game.bn_part[bn][T0].dict2list_vals(valueinput= \
Game.bn_part[bn][T0].value)
#get the list of (m,a) pair from the iterated semi-NFG
mapair = Game.bn_part[bn][T0].get_CPTindex(malist) #get CPT index
r = Game.reward(player,T0) #get the (discounted) reward
if ep != 0: #to avoid "divided by 0" error
r_av_new = r_av + (r-r_av)/((T-1)*ep) #update the dynamic reward
Qmax = Q[mapair] #get the maximum q value
for t in xrange(T0+1,T):
Game.bn_part[bn][t].CPT = convert_2_pureCPT(Q) #convert Q table to CPT
Game.sample_timesteps(t,t) #sample the current time step
if t!= (T-1): #required by Q-learning
r = d**t*Game.reward(player,t) # get the (discounted) reward
r_av_new = r_av + (r-r_av)/((T-1)*ep+t) #update the reward
malist_new = Game.bn_part[bn][t].dict2list_vals(valueinput= \
Game.bn_part[bn][t].value)
mapair_new = Game.bn_part[bn][t].get_CPTindex(malist_new)
visit[mapair] = visit[mapair] + 1 #update the number of times
alpha = (1/(1+visit[mapair]))**w #the learning rate
Qmax_new = Q[mapair_new] #new maximum q value
Q[mapair] = Qmax + alpha*(r + d*Qmax_new -Qmax) #update q table
mapair = mapair_new
Qmax = Qmax_new
r_av = r_av_new
rseries.append(r_av)
self.trained_CPTs[player][bn][level] = Game.bn_part[bn][0].CPT
plt.figure()
plt.plot(rseries, label = str(bn + ' Level ' + str(level)))
#plotting rseries to gauge convergence
plt.legend()
fig = plt.gcf()
self.figs[bn][str(level)] = fig
if setCPT:
map(lambda x: _setallCPTs(self.Game,bn, x, Game.bn_part[bn][0].CPT), np.arange(T0, T))
