def LSPIRmax(D, epsilon, env, policy0, maxiter=10, resample_size=1000, show=False, resample_epsilon=0.1, rmax=1.0):
current = policy0
all_policies = [current]
iters = 0
finished = False
track = TrackKnown(env.nstates, env.nactions, 1)
# TODO: need to couple track object with sample set more tightly.
print "Pre uniq: ", len(D)
D = track.uniq(D)
print "Post uniq: ", len(D)
track.init(D) # initialize knowledge
print "Resample epsilon: ", resample_epsilon
if show:
diagnostics = Diagnostics(env)
while iters < maxiter and not finished:
all_policies.append(current)
# A,b,current,info = LSTDQRmax(D, env, current, track, rmax=rmax)
start_time = time.time()
A, b, current, info = ParallelLSTDQRmax(D, env, current, track, rmax=rmax)
end_time = time.time()
print "Loop time: ", end_time - start_time
policy = partial(env.epsilon_linear_policy, resample_epsilon, current) # need to detect/escape cycles?
# more trace data
t = env.trace(1000, policy=policy, reset_on_cycle=False, reset_on_endstate=False, stop_on_cycle=True)
print "Trace length: ", len(t)
track.resample(D, t, take_all=False) # adds new samples
track.diagnostics()
if show:
print diagnostics(iters, current, A)
# for (i,p) in enumerate(all_policies):
# print "policy: ", i, la.norm(p - current)
iters += 1
print "Iterations: ", iters
for p in all_policies:
if la.norm(p - current) < epsilon and track.all_known():
finished = True
return current, all_policies
def LSPIRmax(D, epsilon, env, policy0, method = "dense", maxiter = 10, resample_size = 1000, show = False, resample_epsilon = 0.1, rmax = 1.0):
current = policy0
all_policies = [current]
iters = 0
finished = False
track = TrackKnown(D, env.nstates, env.nactions, 1)
if show:
diagnostics = Diagnostics(env)
while iters < maxiter and not finished:
print "Iterations: ", iters
all_policies.append(current)
start_time = time.time()
if method is "dense":
A,b,current,info = LSTDQRmax(track, env, current, rmax=rmax)
elif method is "parallel":
A,b,current,info = ParallelLSTDQRmax(track, env, current, rmax=rmax)
else:
raise ValueError, "Unknown LSTDQ method!"
end_time = time.time()
print "Loop time: ", end_time - start_time
policy = partial(env.epsilon_linear_policy, resample_epsilon, current) # need to detect/escape cycles?
# more trace data
t = env.trace(1000, policy = policy, reset_on_cycle = False, reset_on_endstate = False, stop_on_cycle=True)
print "Trace length: ", len(t)
track.extend(t, take_all=False) # adds new samples
track.diagnostics()
if show:
print diagnostics(iters,current,A)
iters += 1
for p in all_policies:
if la.norm(p - current) < epsilon and track.all_known():
finished = True
print "Finished"
return current, all_policies
dict_loop,
(D[i:j], env, w, 0.0)) # note that damping needs to be zero here
results.append(r)
k = len(w)
A = sp.identity(k, format='csr') * damping
b = sp_create(k, 1, 'csr')
for r in results:
T, t = r.get()
A = A + T
b = b + t
# close out the pool of workers
pool.close()
pool.join()
w, info = solve(A, b, method="spsolve")
return A, b, w, info
if __name__ == '__main__':
from gridworld.gridworld8 import SparseGridworld8
import cPickle as pickle
gw = SparseGridworld8(nrows=5, ncols=5, endstates=[0], walls=[])
t = pickle.load(open("/Users/stober/wrk/lspi/bin/rmax_trace.pck"))
policy0 = np.zeros(gw.nfeatures())
track = TrackKnown(t, gw.nstates, gw.nactions, 1)
compare(LSTDQRmax, ParallelLSTDQRmax, track, gw, policy0)
