def policy_gradient_optimize_nesterov(mdp, policy,
gamma,
max_pathlength,
timesteps_per_batch,
n_iter,
stepsize,
beta = .95):
stat2timeseries = defaultdict(list)
widths = (17,10,10,10,10)
print fmt_row(widths, ["EpRewMean","EpLenMean","Perplexity","KLOldNew"])
fprev_sa = policy.f_sa
for i in xrange(n_iter):
total_ts = 0
paths = []
while True:
path = rollout(mdp, policy, max_pathlength)
paths.append(path)
total_ts += pathlength(path)
if total_ts > timesteps_per_batch:
break
# get observations:
obs_no = np.concatenate([path["observations"] for path in paths])
z_sa = policy.f_sa + beta * (policy.f_sa - fprev_sa) # Momemtum term
grad = 0
for path in paths:
n = len(path['rewards'])
q_n = ((path['rewards'] * gamma ** np.arange(n) )[::-1].cumsum())[::-1]
q_n = q_n / gamma ** np.arange(n) # Biased estimator but doesn't decay as fast
grad += softmax_policy_gradient(z_sa, path['observations'],
path['actions'], q_n)
grad = grad / len(paths)
fprev_sa = policy.f_sa
policy.f_sa = z_sa + stepsize * grad
pdists = np.concatenate([path["pdists"] for path in paths])
kl = policy.compute_kl(pdists, policy.compute_pdists(obs_no)).mean()
perplexity = np.exp(policy.compute_entropy(pdists).mean())
stats = { "EpRewMean" : np.mean([path["rewards"].sum() for path in paths]),
"EpRewSEM" : np.std([path["rewards"].sum() for path in paths])/np.sqrt(len(paths)),
"EpLenMean" : np.mean([pathlength(path) for path in paths]),
"Perplexity" : perplexity,
"KLOldNew" : kl }
print fmt_row(widths, ['%.3f+-%.3f'%(stats["EpRewMean"], stats['EpRewSEM']), stats['EpLenMean'], stats['Perplexity'], stats['KLOldNew']])
for (name,val) in stats.items():
stat2timeseries[name].append(val)
return stat2timeseries
# FrozenLake is a MDP with finite state and action that involves navigating across a frozen lake.
# (It's conventionally called a "grid-world" MDP, as the state space involves points in a 2D grid)
# Let's look at the docstring for details
print FrozenLake.__doc__
print "-----------------"
class RandomDiscreteActionChooser(Policy):
def __init__(self, n_actions):
self.n_actions = n_actions
def step(self, observation):
return {"action":np.array([nr.randint(0, self.n_actions)])}
policy = RandomDiscreteActionChooser(mdp.n_actions)
rdata = rollout(mdp, policy, 100)
print rdata
s_n = rdata['observations'] # Vector of states (same as observations since MDP is fully-observed)
a_n = rdata['actions'] # Vector of actions (each is an int in {0,1,2,3})
n = a_n.shape[0] # Length of trajectory
q_n = np.random.randn(n) # Returns (random for the sake of gradient checking)
f_sa = np.random.randn(mdp.n_states, mdp.n_actions) # Policy parameter vector. explained shortly.
def softmax_prob(f_na):
"""
Exponentiate f_na and normalize rows to have sum 1
so each row gives a probability distribution over discrete
action set
"""
prob_nk = np.exp(f_na - f_na.max(axis=1,keepdims=True))
def run_ppo(mdp, policy,
gamma,
max_pathlength,
timesteps_per_batch,
n_iter,
vf = None,
lam=1.0,
penalty_coeff=1.0,
parallel = True,
max_kl = 0.1
):
"""
mdp : instance of MDP
policy : instance of PPOPolicy
max_pathlength : maximum episode length (number of timesteps)
n_iter : number of batches of PPO
vf : instance of ValueFunction
lam : lambda parameter of lambda for computing advantage estimator adv_t = delta_t + (\gamma \lambda) \delta_{t+1} + (\gamma \lambda)^2 \delta_{t+2} + \dots
as described in http://arxiv.org/abs/1506.05254
penalty_coeff : each iteration we solve the unconstrained minimization problem minimize_{theta} L(theta) + penalty_coeff * KL( thetaold, theta )
parallel : use python's multiprocessing to parallelize the rollouts
max_kl : hard limit on KL divergence between old and new policy for one iteration of PPO
"""
assert isinstance(policy, PPOPolicy)
if vf is None: vf = NoValueFunction()
theta = policy.get_parameters_flat()
seed_iter = itertools.count()
start_time = time.time()
numeptotal = 0
for i in xrange(n_iter):
print "********** Iteration %i ************"%i
with Message("Generating paths"):
total_ts = 0
paths = []
if parallel:
# DO ROLLOUTS IN PARALLEL
nproc = multiprocessing.cpu_count()
if sys.platform == "darwin": nproc /= 2
# hyperthreading makes num cpu look twice as high
# but there's no speedup
# store data in global variables so it's accessible from forked processes
# (which start when multiprocessing.Pool is created)
Globals.mdp = mdp
Globals.policy = policy
Globals.max_pathlength = max_pathlength
pool = multiprocessing.Pool(nproc)
pending = []
done = False
while True:
if len(pending) < nproc and not done:
pending.append(pool.apply_async(rollout1, (seed_iter.next(),)))
stillpending = []
for job in pending:
if job.ready():
path = job.get()
paths.append(path)
total_ts += pathlength(path)
else:
stillpending.append(job)
pending = stillpending
if total_ts > timesteps_per_batch:
done = True
if len(pending) == 0:
break
time.sleep(.001)
pool.close()
else:
# EQUIVALENT SERIAL CODE
while True:
path = rollout(mdp, policy, max_pathlength)
paths.append(path)
total_ts += pathlength(path)
if total_ts > timesteps_per_batch:
break
with Message("Computing returns and estimating advantage function"):
allret = []
allb = []
for path in paths:
path["returns"] = discount(path["rewards"], gamma)
b = vf.predict(path)
b1 = np.append(b,0)
# b1 = np.append(b, 0 if path["terminated"] else b[-1])
deltas = path["rewards"] + gamma*b1[1:] - b1[:-1]
path["advantage"] = discount(deltas, gamma*lam)
allb.append(b)
allret.append(path["returns"])
baseline_ev = explained_variance_1d(np.concatenate(allb), np.concatenate(allret))
# baseline_ev = what fraction of variance of returns is explained by the baseline function
# it'll be <= 1; if it's <= 0, it's giving predictions worse than a constant function.
with Message("Updating policy"):
pdist_np = np.concatenate([path["pdists"] for path in paths])
obs_no = np.concatenate([path["observations"] for path in paths])
action_na = np.concatenate([path["actions"] for path in paths])
# Standardize the advantage function to have mean=0 and std=1
advantage_n = np.concatenate([path["advantage"] for path in paths])
advantage_n -= advantage_n.mean()
advantage_n /= (advantage_n.std()+1e-8)
assert obs_no.shape[0] == pdist_np.shape[0] == action_na.shape[0] == advantage_n.shape[0]
n_train_paths = int(len(paths)*.75)
train_sli = slice(0,sum(pathlength(path) for path in paths[:n_train_paths]))
test_sli = slice(train_sli.stop, None)
# Training/test split
poar_train, poar_test = [tuple(arr[sli] for arr in (pdist_np, obs_no, action_na, advantage_n)) for sli in (train_sli, test_sli)]
obj_names = ["L","KL"]
obj_before_train = policy.compute_surr_kl(*poar_train)
obj_before_test = policy.compute_surr_kl(*poar_test)
def fpen(th):
thprev = policy.get_parameters_flat()
policy.set_parameters_flat(th)
surr, kl = policy.compute_surr_kl(*poar_train) #pylint: disable=W0640
out = penalty_coeff * kl - surr
if kl > max_kl or not np.isfinite(out):
out = 1e10
# testsurr = policy.compute_surr_kl(*poar_test)[0]
# print "dtheta norm",np.linalg.norm(th - theta),"train lagrangian",out
# print "testsurr improvement",testsurr - obj_before_test[0]
policy.set_parameters_flat(thprev)
return out
def fgradpen(th):
thprev = policy.get_parameters_flat()
policy.set_parameters_flat(th)
out = - policy.compute_grad_lagrangian(penalty_coeff, *poar_train) #pylint: disable=W0640
policy.set_parameters_flat(thprev)
return out
theta,_,info = scipy.optimize.fmin_l_bfgs_b(fpen, theta, fprime=fgradpen, maxiter=20)
del info["grad"]
print info
policy.set_parameters_flat(theta)
obj_after_train = policy.compute_surr_kl(*poar_train)
obj_after_test = policy.compute_surr_kl(*poar_test)
delta_train = np.array(obj_after_train) - np.array(obj_before_train)
delta_test = np.array(obj_after_test) - np.array(obj_before_test)
with Message("Computing baseline function for next iter"):
vf.fit(paths)
with Message("Computing stats"):
episoderewards = np.array([path["rewards"].sum() for path in paths])
pathlengths = np.array([pathlength(path) for path in paths])
entropy = policy.compute_entropy(pdist_np).mean()
perplexity = np.exp(entropy)
stats = OrderedDict()
for (name, dtrain, dtest) in zip(obj_names, delta_train, delta_test):
stats.update({
u"Train_d"+name : dtrain,
u"Test_d"+name : dtest,
# u"Ratio_"+name : dtest/dtrain
})
# stats["Armijo"] = (obj_after_train[0] - obj_before_train[0]) / step.dot(g)
numeptotal += len(episoderewards)
stats["NumEpBatch"] = len(episoderewards)
stats["NumEpTotal"] = numeptotal
stats["EpRewMean"] = episoderewards.mean()
stats["EpRewSEM"] = episoderewards.std()/np.sqrt(len(paths))
stats["EpRewMax"] = episoderewards.max()
stats["EpLenMean"] = pathlengths.mean()
stats["EpLenMax"] = pathlengths.max()
stats["RewPerStep"] = episoderewards.sum()/pathlengths.sum()
stats["BaselineEV"] = baseline_ev
stats["Entropy"] = entropy
stats["Perplexity"] = perplexity
stats["TimeElapsed"] = time.time() - start_time
yield stats
def rollout1(seed):
np.random.seed(seed)
return rollout(Globals.mdp, Globals.policy, Globals.max_pathlength)
def run_ppo(mdp,
policy,
gamma,
max_pathlength,
timesteps_per_batch,
n_iter,
vf=None,
lam=1.0,
penalty_coeff=1.0,
parallel=True,
max_kl=0.1):
"""
mdp : instance of MDP
policy : instance of PPOPolicy
max_pathlength : maximum episode length (number of timesteps)
n_iter : number of batches of PPO
vf : instance of ValueFunction
lam : lambda parameter of lambda for computing advantage estimator adv_t = delta_t + (\gamma \lambda) \delta_{t+1} + (\gamma \lambda)^2 \delta_{t+2} + \dots
as described in http://arxiv.org/abs/1506.05254
penalty_coeff : each iteration we solve the unconstrained minimization problem minimize_{theta} L(theta) + penalty_coeff * KL( thetaold, theta )
parallel : use python's multiprocessing to parallelize the rollouts
max_kl : hard limit on KL divergence between old and new policy for one iteration of PPO
"""
assert isinstance(policy, PPOPolicy)
if vf is None: vf = NoValueFunction()
theta = policy.get_parameters_flat()
seed_iter = itertools.count()
start_time = time.time()
numeptotal = 0
for i in xrange(n_iter):
print "********** Iteration %i ************" % i
with Message("Generating paths"):
total_ts = 0
paths = []
if parallel:
# DO ROLLOUTS IN PARALLEL
nproc = multiprocessing.cpu_count()
if sys.platform == "darwin": nproc /= 2
# hyperthreading makes num cpu look twice as high
# but there's no speedup
# store data in global variables so it's accessible from forked processes
# (which start when multiprocessing.Pool is created)
Globals.mdp = mdp
Globals.policy = policy
Globals.max_pathlength = max_pathlength
pool = multiprocessing.Pool(nproc)
pending = []
done = False
while True:
if len(pending) < nproc and not done:
pending.append(
pool.apply_async(rollout1, (seed_iter.next(), )))
stillpending = []
for job in pending:
if job.ready():
path = job.get()
paths.append(path)
total_ts += pathlength(path)
else:
stillpending.append(job)
pending = stillpending
if total_ts > timesteps_per_batch:
done = True
if len(pending) == 0:
break
time.sleep(.001)
pool.close()
else:
# EQUIVALENT SERIAL CODE
while True:
path = rollout(mdp, policy, max_pathlength)
paths.append(path)
total_ts += pathlength(path)
if total_ts > timesteps_per_batch:
break
with Message("Computing returns and estimating advantage function"):
allret = []
allb = []
for path in paths:
path["returns"] = discount(path["rewards"], gamma)
b = vf.predict(path)
b1 = np.append(b, 0)
# b1 = np.append(b, 0 if path["terminated"] else b[-1])
deltas = path["rewards"] + gamma * b1[1:] - b1[:-1]
path["advantage"] = discount(deltas, gamma * lam)
allb.append(b)
allret.append(path["returns"])
baseline_ev = explained_variance_1d(np.concatenate(allb),
np.concatenate(allret))
# baseline_ev = what fraction of variance of returns is explained by the baseline function
# it'll be <= 1; if it's <= 0, it's giving predictions worse than a constant function.
with Message("Updating policy"):
pdist_np = np.concatenate([path["pdists"] for path in paths])
obs_no = np.concatenate([path["observations"] for path in paths])
action_na = np.concatenate([path["actions"] for path in paths])
# Standardize the advantage function to have mean=0 and std=1
advantage_n = np.concatenate([path["advantage"] for path in paths])
advantage_n -= advantage_n.mean()
advantage_n /= (advantage_n.std() + 1e-8)
assert obs_no.shape[0] == pdist_np.shape[0] == action_na.shape[
0] == advantage_n.shape[0]
n_train_paths = int(len(paths) * .75)
train_sli = slice(
0, sum(pathlength(path) for path in paths[:n_train_paths]))
test_sli = slice(train_sli.stop, None)
# Training/test split
poar_train, poar_test = [
tuple(arr[sli]
for arr in (pdist_np, obs_no, action_na, advantage_n))
for sli in (train_sli, test_sli)
]
obj_names = ["L", "KL"]
obj_before_train = policy.compute_surr_kl(*poar_train)
obj_before_test = policy.compute_surr_kl(*poar_test)
def fpen(th):
thprev = policy.get_parameters_flat()
policy.set_parameters_flat(th)
surr, kl = policy.compute_surr_kl(*poar_train) #pylint: disable=W0640
out = penalty_coeff * kl - surr
if kl > max_kl or not np.isfinite(out):
out = 1e10
# testsurr = policy.compute_surr_kl(*poar_test)[0]
# print "dtheta norm",np.linalg.norm(th - theta),"train lagrangian",out
# print "testsurr improvement",testsurr - obj_before_test[0]
policy.set_parameters_flat(thprev)
return out
def fgradpen(th):
thprev = policy.get_parameters_flat()
policy.set_parameters_flat(th)
out = -policy.compute_grad_lagrangian(penalty_coeff, *
poar_train) #pylint: disable=W0640
policy.set_parameters_flat(thprev)
return out
theta, _, info = scipy.optimize.fmin_l_bfgs_b(fpen,
theta,
fprime=fgradpen,
maxiter=20)
del info["grad"]
print info
policy.set_parameters_flat(theta)
obj_after_train = policy.compute_surr_kl(*poar_train)
obj_after_test = policy.compute_surr_kl(*poar_test)
delta_train = np.array(obj_after_train) - np.array(
obj_before_train)
delta_test = np.array(obj_after_test) - np.array(obj_before_test)
with Message("Computing baseline function for next iter"):
vf.fit(paths)
with Message("Computing stats"):
episoderewards = np.array(
[path["rewards"].sum() for path in paths])
pathlengths = np.array([pathlength(path) for path in paths])
entropy = policy.compute_entropy(pdist_np).mean()
perplexity = np.exp(entropy)
stats = OrderedDict()
for (name, dtrain, dtest) in zip(obj_names, delta_train,
delta_test):
stats.update({
u"Train_d" + name: dtrain,
u"Test_d" + name: dtest,
# u"Ratio_"+name : dtest/dtrain
})
# stats["Armijo"] = (obj_after_train[0] - obj_before_train[0]) / step.dot(g)
numeptotal += len(episoderewards)
stats["NumEpBatch"] = len(episoderewards)
stats["NumEpTotal"] = numeptotal
stats["EpRewMean"] = episoderewards.mean()
stats["EpRewSEM"] = episoderewards.std() / np.sqrt(len(paths))
stats["EpRewMax"] = episoderewards.max()
stats["EpLenMean"] = pathlengths.mean()
stats["EpLenMax"] = pathlengths.max()
stats["RewPerStep"] = episoderewards.sum() / pathlengths.sum()
stats["BaselineEV"] = baseline_ev
stats["Entropy"] = entropy
stats["Perplexity"] = perplexity
stats["TimeElapsed"] = time.time() - start_time
yield stats
def rollout1(seed):
np.random.seed(seed)
return rollout(Globals.mdp, Globals.policy, Globals.max_pathlength)
