def interpolate_landscape(env, policy, param_1, param_2, horizon, disc = 0.99, episodes = 100, resolution = 0.01, fun=None):
perfs = []
lams = np.arange(0., 1. + resolution, resolution)
for lam in lams:
param = (1 - lam) * param_1 + lam * param_2
policy.set_from_flat(param)
if fun is None:
batch = generate_batch(env, policy, horizon, episodes)
perfs.append(performance(batch, disc))
else:
perfs.append(fun(param))
plt.plot(lams, perfs)
plt.show()
def play(env,
policy,
horizon=100,
episodes=100,
render=True,
action_filter=None):
if action_filter is None:
action_filter = clip(env)
return generate_batch(env,
policy,
horizon,
episodes,
render=render,
action_filter=action_filter)
def adabatch(env, policy,
horizon,
batchsize = 100,
iterations = 1000,
gamma = 0.99,
rmax = 1.,
phimax = 1.,
safety_requirement = MonotonicImprovement(),
test_det = True,
render = False,
seed = None,
baseline = 'peters',
action_filter = None,
parallel = False,
n_jobs = 4,
logger = Logger(name='test_sunday'),
save_params = 1000,
log_params = True,
verbose = True):
"""
Only for SIMPLE Gaussian policy w/ scalar variance
"""
# Defaults
assert policy.learn_std
if action_filter is None:
action_filter = clip(env)
# Seeding agent
if seed is not None:
seed_all_agent(seed)
# Preparing logger
algo_info = {'Algorithm': 'ADASTEP',
'Environment': str(env),
'BatchSize': batchsize,
'Max horizon': horizon,
'Iterations': iterations,
'gamma': gamma,
'actionFilter': action_filter,
'rmax': rmax,
'phimax': phimax}
logger.write_info({**algo_info, **policy.info()})
log_keys = ['Perf', 'UPerf', 'AvgHorizon',
'Alpha', 'BatchSize', 'Exploration',
'ThetaGradNorm',
'Penalty', 'Coordinate']
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if test_det:
log_keys.append('DetPerf')
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
# Learning
avol = torch.tensor(env.action_space.high - env.action_space.low).item()
it = 0
while(it < iterations):
# Begin iteration
if verbose:
print('\nIteration ', it)
if verbose:
print('Params: ', policy.get_flat())
# Test
if test_det:
omega = policy.get_scale_params()
policy.set_scale_params(-100.)
batch = generate_batch(env, policy, horizon, 1, action_filter)
policy.set_scale_params(omega)
log_row['DetPerf'] = performance(batch, gamma)
if render:
generate_batch(env, policy, horizon, 1, action_filter, render=True)
omega = policy.get_scale_params()
sigma = torch.exp(omega).item()
batch = generate_batch(env, policy, horizon, batchsize, action_filter, parallel=parallel, n_jobs=n_jobs, seed=seed)
grad = simple_gpomdp_estimator(batch, gamma, policy, baseline)
theta_grad = grad[1:]
norminf = torch.max(torch.abs(theta_grad))
k = torch.argmax(torch.abs(theta_grad))
penalty = rmax * phimax**2 / (1-gamma)**2 * (avol / (sigma * math.sqrt(2*math.pi)) + gamma / (2*(1-gamma)))
alpha_star = sigma ** 2/ (2 * penalty)
Cmax = alpha_star * norminf*2 / 2
C = safety_requirement.next()
alpha = alpha_star * (1 + math.sqrt(1 - C / (Cmax + 1e-12) + 1e-12))
theta = policy.get_loc_params()
new_theta = theta
new_theta[k] += alpha * theta_grad[k]
policy.set_loc_params(new_theta)
# Log
log_row['Coordinate'] = k.item()
log_row['Alpha'] = alpha
log_row['Penalty'] = penalty
log_row['ThetaGradNorm'] = torch.norm(theta_grad).item()
log_row['BatchSize'] = batchsize
log_row['Exploration'] = policy.exploration()
log_row['Alpha'] = alpha
log_row['Perf'] = performance(batch, gamma)
log_row['UPerf'] = performance(batch, 1.)
log_row['AvgHorizon'] = avg_horizon(batch)
params = policy.get_flat()
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
logger.write_row(log_row, it)
if save_params and it % save_params == 0:
logger.save_params(params, it)
# Next iteration
it += 1
# Final policy
if save_params:
logger.save_params(params, it)
def adastep(env, policy,
horizon,
batchsize = 100,
iterations = 1000,
gamma = 0.99,
rmax = 1.,
phimax = 1.,
greedy = True,
delta = 1.,
test_det = True,
render = False,
seed = None,
baseline = 'peters',
action_filter = None,
parallel = False,
n_jobs = 4,
logger = Logger(name='test_sunday'),
save_params = 1000,
log_params = True,
verbose = True):
"""
Only for SIMPLE Gaussian policy w/ scalar variance
Policy must have learn_std = False, as std is META-learned
"""
# Defaults
assert policy.learn_std
if action_filter is None:
action_filter = clip(env)
# Seeding agent
if seed is not None:
seed_all_agent(seed)
# Preparing logger
algo_info = {'Algorithm': 'ADASTEP',
'Environment': str(env),
'BatchSize': batchsize,
'Max horizon': horizon,
'Iterations': iterations,
'gamma': gamma,
'actionFilter': action_filter,
'rmax': rmax,
'phimax': phimax,
'greedy': greedy}
logger.write_info({**algo_info, **policy.info()})
log_keys = ['Perf', 'UPerf', 'AvgHorizon',
'Alpha', 'BatchSize', 'Exploration',
'ThetaGradNorm',
'Penalty']
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if test_det:
log_keys.append('DetPerf')
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
# Learning
avol = torch.tensor(env.action_space.high - env.action_space.low).item()
it = 0
while(it < iterations):
# Begin iteration
if verbose:
print('\nIteration ', it)
if verbose:
print('Params: ', policy.get_flat())
# Test
if test_det:
omega = policy.get_scale_params()
policy.set_scale_params(-100.)
batch = generate_batch(env, policy, horizon, 1, action_filter)
policy.set_scale_params(omega)
log_row['DetPerf'] = performance(batch, gamma)
if render:
generate_batch(env, policy, horizon, 1, action_filter, render=True)
omega = policy.get_scale_params()
sigma = torch.exp(omega).item()
batch = generate_batch(env, policy, horizon, batchsize, action_filter, parallel=parallel, n_jobs=n_jobs, seed=seed)
if delta < 1:
grad, grad_var = simple_gpomdp_estimator(batch, gamma, policy, baseline, result='moments')
theta_grad = grad[1:]
theta_grad_var = grad_var[1:]
quant = 2*sts.t.interval(1 - delta, batchsize-1,loc=0.,scale=1.)[1]
eps = quant * torch.sqrt(theta_grad_var / batchsize + 1e-12)
norm2 = torch.norm(torch.clamp(torch.abs(theta_grad) - eps, min=0.))
norm1 = torch.sum(torch.abs(theta_grad) + eps)
else:
grad = simple_gpomdp_estimator(batch, gamma, policy, baseline)
theta_grad = grad[1:]
norm2 = torch.norm(theta_grad)
norm1 = torch.sum(torch.abs(theta_grad))
penalty = rmax * phimax**2 / (1-gamma)**2 * (avol / (sigma * math.sqrt(2*math.pi)) + gamma / (2*(1-gamma)))
alpha_star = sigma ** 2 * norm2 ** 2 / (2 * penalty * norm1 ** 2 + 1e-12)
Cmax = alpha_star * norm2**2 / 2
if greedy:
C = Cmax
else:
C = 0
alpha = alpha_star * (1 + math.sqrt(1 - C / (Cmax + 1e-12) + 1e-12))
theta = policy.get_loc_params()
new_theta = theta + alpha * theta_grad
policy.set_loc_params(new_theta)
# Log
log_row['Alpha'] = alpha
log_row['Penalty'] = penalty
log_row['ThetaGradNorm'] = torch.norm(theta_grad).item()
log_row['BatchSize'] = batchsize
log_row['Exploration'] = policy.exploration()
log_row['Alpha'] = alpha.item()
log_row['Perf'] = performance(batch, gamma)
log_row['UPerf'] = performance(batch, 1.)
log_row['AvgHorizon'] = avg_horizon(batch)
params = policy.get_flat()
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
logger.write_row(log_row, it)
if save_params and it % save_params == 0:
logger.save_params(params, it)
# Next iteration
it += 1
# Final policy
if save_params:
logger.save_params(params, it)
def sepg(env, policy,
horizon,
batchsize = 100,
iterations = 1000,
gamma = 0.99,
rmax = 1.,
phimax = 1.,
safety_requirement = 'mi',
delta = 1.,
confidence_schedule = None,
clip_at = 100,
test_batchsize = False,
render = False,
seed = None,
baseline = 'peters',
shallow = True,
action_filter = None,
parallel = False,
logger = Logger(name='SEPG'),
save_params = 1000,
log_params = True,
verbose = True):
"""
Only for SIMPLE Gaussian policy w/ scalar variance
Policy must have learn_std = False, as std is META-learned
"""
#Defaults
assert policy.learn_std
if action_filter is None:
action_filter = clip(env)
#Seed agent
if seed is not None:
seed_all_agent(seed)
#Prepare logger
algo_info = {'Algorithm': 'SEPG',
'Environment': str(env),
'BatchSize': batchsize,
'Max horizon': horizon,
'Iterations': iterations,
'gamma': gamma,
'actionFilter': action_filter,
'rmax': rmax,
'phimax': phimax}
logger.write_info({**algo_info, **policy.info()})
log_keys = ['Perf', 'UPerf', 'AvgHorizon',
'Alpha', 'BatchSize', 'Exploration', 'Eta',
'ThetaGradNorm', 'OmegaGrad', 'OmegaMetagrad',
'Penalty', 'MetaPenalty',
'IterationKind',
'ThetaGradNorm', 'Eps', 'Up', 'Down', 'C', 'Cmax', 'Delta'] #0: theta, 1: omega
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if test_batchsize:
log_keys.append('DetPerf')
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
#Safety requirements
if safety_requirement == 'mi':
thresholder = MonotonicImprovement()
elif safety_requirement == 'budget':
batch = generate_batch(env, policy, horizon, batchsize, action_filter)
thresholder = Budget(performance(batch, gamma))
else:
thresholder = FixedThreshold(float(safety_requirement))
#Learning loop
omega_grad = float('nan')
omega_metagrad = float('nan')
metapenalty = float('nan')
eta = float('nan')
it = 0
while(it < iterations):
#Begin iteration
if verbose:
print('\nIteration ', it)
if verbose:
print('Params: ', policy.get_flat())
#Test mean parameters on deterministic policy
if test_batchsize:
test_batch = generate_batch(env, policy, horizon, test_batchsize,
action_filter=action_filter,
seed=seed,
njobs=parallel,
deterministic=True)
log_row['DetPerf'] = performance(test_batch, gamma)
#Render behavior
if render:
generate_batch(env, policy, horizon, 1, action_filter, render=True)
#
if it % 2 == 0:
#Std update
omega = policy.get_scale_params()
sigma = torch.exp(omega).item()
batch = generate_batch(env, policy, horizon, batchsize,
action_filter=action_filter,
njobs=parallel,
seed=seed)
if confidence_schedule is not None:
delta = confidence_schedule.next(it)
log_row['Delta'] = delta
if delta <1:
grad, grad_var = simple_gpomdp_estimator(batch, gamma, policy, baseline, result='moments')
omega_grad = grad[0]
omega_grad_var = grad_var[0]
omega_metagrad, omega_metagrad_var = metagrad(batch, gamma, policy, alpha, clip_at, baseline, result='moments')
quant = 2 * sts.t.interval(1 - delta, batchsize-1,loc=0.,scale=1.)[1]
eps = torch.tensor(quant * torch.sqrt(omega_grad_var / batchsize), dtype=torch.float)
log_row['Eps'] = torch.norm(eps).item()
metaeps = torch.tensor(quant * torch.sqrt(omega_metagrad_var / batchsize), dtype=torch.float)
if torch.sign(omega_grad).item() >= 0 and torch.sign(omega_metagrad).item() >= 0:
up = torch.clamp(torch.abs(omega_grad - eps), min=0.) * torch.clamp(torch.abs(omega_metagrad - metaeps), min=0.)
elif torch.sign(omega_grad).item() >= 0 and torch.sign(omega_metagrad).item() < 0:
up = (omega_grad + eps) * (omega_metagrad - metaeps)
elif torch.sign(omega_grad).item() < 0 and torch.sign(omega_metagrad).item() >=0:
up = (omega_grad - eps) * (omega_metagrad + eps)
else:
up = torch.abs(omega_grad + eps) * torch.abs(omega_metagrad + metaeps)
down = omega_metagrad + metaeps * torch.sign(omega_metagrad)
log_row['Up'] = up.item()
log_row['Down'] = down.item()
metapenalty = rmax / (1 - gamma)**2 * (0.53 * avol / (2 * sigma) + gamma / (1 - gamma))
eta_star = (up / (2 * metapenalty * down**2 + 1e-12)).item()
Cmax = up**2 / (4 * metapenalty * down**2).item()
else:
log_row['Eps'] = 0
grad = gpomdp_estimator(batch, gamma, policy,
baselinekind=baseline,
shallow=shallow)
theta_grad = grad[1:]
omega_grad = grad[0]
#->
mixed, _ = mixed_estimator(batch, gamma, policy, baseline, theta_grad)
norm_grad = 2 * theta_grad.dot(mixed)
A = omega_grad
B = 2 * alpha * torch.norm(theta_grad)**2
C = sigma * alpha * norm_grad
C = torch.clamp(C, min=-clip_at, max=clip_at)
omega_metagrad = A + B + C
metapenalty = rmax / (1 - gamma)**2 * (0.53 * avol / (2 * sigma) + gamma / (1 - gamma))
eta_star = (omega_grad / (2 * metapenalty * omega_metagrad) + 1e-12).item()
Cmax = (omega_grad ** 2 / (4 * metapenalty)).item()
log_row['Up'] = torch.tensor(omega_grad).item()
log_row['Down'] = torch.tensor(omega_metagrad).item()
perf = performance(batch, gamma)
Co = thresholder.next(perf)
Co = min(Co, Cmax)
log_row['C'] = Co
log_row['Cmax'] = Cmax
eta = eta_star + abs(eta_star) * math.sqrt(1 - Co / (Cmax + 1e-12) + 1e-12)
new_omega = omega + eta * omega_metagrad
policy.set_scale_params(new_omega)
###
else:
#Mean update
omega = policy.get_scale_params()
sigma = torch.exp(omega).item()
batch = generate_batch(env, policy, horizon, batchsize,
action_filter=action_filter,
n_jobs=parallel,
seed=seed)
if confidence_schedule is not None:
delta = confidence_schedule.next(it)
log_row['Delta'] = delta
if delta < 1:
grad, grad_var = simple_gpomdp_estimator(batch, gamma, policy, baseline, result='moments')
theta_grad = grad[1:]
theta_grad_var = grad_var[1:]
quant = 2*sts.t.interval(1 - delta, batchsize-1,loc=0.,scale=1.)[1]
eps = quant * torch.sqrt(theta_grad_var / batchsize)
log_row['Eps'] = torch.norm(eps).item()
norm2 = torch.norm(torch.clamp(torch.abs(theta_grad) - eps, min=0.))
norm1 = torch.sum(torch.abs(theta_grad) + eps)
log_row['Up'] = norm1.item()
log_row['Down'] = norm2.item()
else:
log_row['Eps'] = 0
grad = simple_gpomdp_estimator(batch, gamma, policy, baseline)
theta_grad = grad[1:]
norm2 = torch.norm(theta_grad)
norm1 = torch.sum(torch.abs(theta_grad))
log_row['Up'] = norm1.item()
log_row['Down'] = norm2.item()
penalty = rmax * phimax**2 / (1-gamma)**2 * (avol / (sigma * math.sqrt(2*math.pi)) + gamma / (2*(1-gamma)))
alpha_star = sigma ** 2 * norm2 ** 2 / (2 * penalty * norm1 ** 2 + 1e-12)
Cmax = (alpha_star * norm2**2 / 2).item()
perf = performance(batch, gamma)
Co = thresholder.next(perf)
Co = min(Co, Cmax)
log_row['C'] = Co
log_row['Cmax'] = Cmax
alpha = alpha_star * (1 + math.sqrt(1 - Co / (Cmax + 1e-12) + 1e-12))
theta = policy.get_loc_params()
new_theta = theta + alpha * theta_grad
policy.set_loc_params(new_theta)
###
# Log
log_row['IterationKind'] = it % 2
log_row['ThetaGradNorm'] = torch.norm(theta_grad).item()
log_row['Alpha'] = alpha
log_row['Eta'] = eta
log_row['Penalty'] = penalty
log_row['MetaPenalty'] = metapenalty
log_row['OmegaGrad'] = torch.tensor(omega_grad).item()
log_row['OmegaMetagrad'] = torch.tensor(omega_metagrad).item()
log_row['ThetaGradNorm'] = torch.norm(theta_grad).item()
log_row['BatchSize'] = batchsize
log_row['Exploration'] = policy.exploration()
log_row['Alpha'] = alpha.item()
log_row['Perf'] = perf
log_row['UPerf'] = performance(batch, 1.)
log_row['AvgHorizon'] = avg_horizon(batch)
params = policy.get_flat()
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
logger.write_row(log_row, it)
if save_params and it % save_params == 0:
logger.save_params(params, it)
# Next iteration
it += 1
# Final policy
if save_params:
logger.save_params(params, it)
def reinforce2(alpha, logsig, env, policy, horizon, *,
batchsize=100,
iterations=1000,
disc=0.99,
stepper=ConstantStepper(1e-2),
action_filter=None,
estimator='gpomdp',
baseline='avg',
logger=Logger(name='gpomdp'),
shallow=False,
seed=None,
test_batchsize=False,
info_key='danger',
save_params=100,
log_params=False,
log_grad=False,
parallel=False,
render=False,
verbose=1):
"""
REINFORCE/G(PO)MDP algorithmn
env: environment
policy: the one to improve
horizon: maximum task horizon
batchsize: number of trajectories used to estimate policy gradient
iterations: number of policy updates
disc: discount factor
stepper: step size criterion. A constant step size is used by default
action_filter: function to apply to the agent's action before feeding it to
the environment, not considered in gradient estimation. By default,
the action is clipped to satisfy evironmental boundaries
estimator: either 'reinforce' or 'gpomdp' (default). The latter typically
suffers from less variance
baseline: control variate to be used in the gradient estimator. Either
'avg' (average reward, default), 'peters' (variance-minimizing) or
'zero' (no baseline)
logger: for human-readable logs (standard output, csv, tensorboard...)
shallow: whether to employ pre-computed score functions (only available for
shallow policies)
seed: random seed (None for random behavior)
test_batchsize: number of test trajectories used to evaluate the
corresponding deterministic policy at each iteration. If 0 or False, no
test is performed
save_params: how often (every x iterations) to save the policy
parameters to disk. Final parameters are always saved for
x>0. If False, they are never saved.
log_params: whether to include policy parameters in the human-readable logs
log_grad: whether to include gradients in the human-readable logs
parallel: number of parallel jobs for simulation. If 0 or False,
sequential simulation is performed.
render: how often (every x iterations) to render the agent's behavior
on a sample trajectory. If False, no rendering happens
verbose: level of verbosity (0: only logs; 1: normal; 2: maximum)
"""
# Defaults
if action_filter is None:
action_filter = clip(env)
# Seed agent
if seed is not None:
seed_all_agent(seed)
# Prepare logger
algo_info = {'Algorithm': 'REINFORCE',
'Estimator': estimator,
'Baseline': baseline,
'Env': str(env),
'Horizon': horizon,
'BatchSize': batchsize,
'Disc': disc,
'StepSizeCriterion': str(stepper),
'Seed': seed,
}
logger.write_info({**algo_info, **policy.info()})
log_keys = ['Perf',
'UPerf',
'AvgHorizon',
'StepSize',
'GradNorm',
'Time',
'StepSize',
'Exploration',
'Info']
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if log_grad:
log_keys += ['grad%d' % i for i in range(policy.num_params())]
if test_batchsize:
log_keys += ['TestPerf', 'TestPerf', 'TestInfo']
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
# init image & csv
filename = "../csv/minigolf/REINFORCE/ALPHA={}/LOGSTD={}/data{}.csv".format(alpha, logsig, seed)
os.makedirs(os.path.dirname(filename), exist_ok=True)
data_file = open(filename, mode='w')
file_writer = csv.writer(data_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
visualizer = MGVisualizer("MG visualizer", "/minigolf/REINFORCE/ALPHA={}/LOGSTD={}/test{}.png".format(alpha, logsig,
seed))
visualizer.clean_panels()
# PLOTTER INFO
stats = {}
stats['w1'] = []
stats['w2'] = []
stats['w3'] = []
stats['w4'] = []
stats['j'] = []
stats['fail'] = []
# ------------
# Learning loop
it = 0
cumulative_fail = 0
cumulative_j = 0
while it < iterations:
# Begin iteration
start = time.time()
if verbose:
print('\nIteration ', it)
params = policy.get_flat()
if verbose > 1:
print('Parameters:', params)
# Test the corresponding deterministic policy
if test_batchsize:
test_batch = generate_batch(env, policy, horizon, test_batchsize,
action_filter=action_filter,
seed=seed,
njobs=parallel,
deterministic=True,
key=info_key)
log_row['TestPerf'] = performance(test_batch, disc)
log_row['TestInfo'] = mean_sum_info(test_batch).item()
log_row['UTestPerf'] = performance(test_batch, 1)
# Render the agent's behavior
if render and it % render == 0:
generate_batch(env, policy, horizon,
episodes=1,
action_filter=action_filter,
render=True,
key=info_key)
# Collect trajectories
batch = generate_batch(env, policy, horizon, batchsize,
action_filter=action_filter,
seed=seed,
n_jobs=parallel,
key=info_key)
# ------------------- count fails -------------------
rewards = [b[2] for b in batch]
failures = [np.count_nonzero(r==-100) for r in rewards]
cumulative_fail += sum(failures)
# ---------------------------------------------------
perf = performance(batch, disc)
cumulative_j += perf
log_row['Perf'] = perf
log_row['Info'] = mean_sum_info(batch).item()
log_row['UPerf'] = performance(batch, disc=1.)
log_row['AvgHorizon'] = avg_horizon(batch)
log_row['Exploration'] = policy.exploration().item()
log_row['IterationFails'] = sum(failures)
log_row['CumulativeFails'] = cumulative_fail
# Estimate policy gradient
if estimator == 'gpomdp':
grad = gpomdp_estimator(batch, disc, policy,
baselinekind=baseline,
shallow=shallow)
elif estimator == 'reinforce':
grad = reinforce_estimator(batch, disc, policy,
baselinekind=baseline,
shallow=shallow)
else:
raise ValueError('Invalid policy gradient estimator')
if verbose > 1:
print('Gradients: ', grad)
log_row['GradNorm'] = torch.norm(grad).item()
# Select meta-parameters
stepsize = stepper.next(grad)
log_row['StepSize'] = torch.norm(torch.tensor(stepsize)).item()
# Update policy parameters
new_params = params + stepsize * grad
policy.set_from_flat(new_params)
# Log
log_row['Time'] = time.time() - start
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
if log_grad:
for i in range(policy.num_params()):
log_row['grad%d' % i] = grad[i].item()
logger.write_row(log_row, it)
# Save parameters
if save_params and it % save_params == 0:
logger.save_params(params, it)
print(new_params)
params = new_params.numpy()[1:] # updated w
# update csv & image
visualizer.show_values(params, perf, cumulative_fail)
file_writer.writerow([params[0], params[1], params[2], params[3], cumulative_fail, perf])
# PLOTTER INFO
# if it % 10 == 0:
stats['w1'].append(params[0])
stats['w2'].append(params[1])
stats['w3'].append(params[2])
stats['w4'].append(params[3])
stats['j'].append(perf)
stats['fail'].append(cumulative_fail)
# ------------
# Next iteration
it += 1
# Save final parameters
if save_params:
logger.save_params(params, it)
visualizer.save_image()
# Cleanup
logger.close()
return stats, cumulative_j
def reinforce(env,
policy,
horizon,
*,
batchsize=100,
iterations=1000,
disc=0.99,
stepper=ConstantStepper(1e-2),
action_filter=None,
estimator='gpomdp',
baseline='avg',
logger=Logger(name='gpomdp'),
shallow=False,
seed=None,
test_batchsize=False,
info_key='danger',
save_params=100,
log_params=False,
log_grad=False,
parallel=False,
render=False,
verbose=1):
"""
REINFORCE/G(PO)MDP algorithmn
env: environment
policy: the one to improve
horizon: maximum task horizon
batchsize: number of trajectories used to estimate policy gradient
iterations: number of policy updates
disc: discount factor
stepper: step size criterion. A constant step size is used by default
action_filter: function to apply to the agent's action before feeding it to
the environment, not considered in gradient estimation. By default,
the action is clipped to satisfy evironmental boundaries
estimator: either 'reinforce' or 'gpomdp' (default). The latter typically
suffers from less variance
baseline: control variate to be used in the gradient estimator. Either
'avg' (average reward, default), 'peters' (variance-minimizing) or
'zero' (no baseline)
logger: for human-readable logs (standard output, csv, tensorboard...)
shallow: whether to employ pre-computed score functions (only available for
shallow policies)
seed: random seed (None for random behavior)
test_batchsize: number of test trajectories used to evaluate the
corresponding deterministic policy at each iteration. If 0 or False, no
test is performed
save_params: how often (every x iterations) to save the policy
parameters to disk. Final parameters are always saved for
x>0. If False, they are never saved.
log_params: whether to include policy parameters in the human-readable logs
log_grad: whether to include gradients in the human-readable logs
parallel: number of parallel jobs for simulation. If 0 or False,
sequential simulation is performed.
render: how often (every x iterations) to render the agent's behavior
on a sample trajectory. If False, no rendering happens
verbose: level of verbosity (0: only logs; 1: normal; 2: maximum)
"""
#Defaults
if action_filter is None:
action_filter = clip(env)
#Seed agent
if seed is not None:
seed_all_agent(seed)
#Prepare logger
algo_info = {
'Algorithm': 'REINFORCE',
'Estimator': estimator,
'Baseline': baseline,
'Env': str(env),
'Horizon': horizon,
'BatchSize': batchsize,
'Disc': disc,
'StepSizeCriterion': str(stepper),
'Seed': seed,
}
logger.write_info({**algo_info, **policy.info()})
log_keys = [
'Perf', 'UPerf', 'AvgHorizon', 'StepSize', 'GradNorm', 'Time',
'StepSize', 'Exploration', 'Info'
]
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if log_grad:
log_keys += ['grad%d' % i for i in range(policy.num_params())]
if test_batchsize:
log_keys += ['TestPerf', 'TestPerf', 'TestInfo']
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
#Learning loop
it = 0
while (it < iterations):
#Begin iteration
start = time.time()
if verbose:
print('\nIteration ', it)
params = policy.get_flat()
if verbose > 1:
print('Parameters:', params)
#Test the corresponding deterministic policy
if test_batchsize:
test_batch = generate_batch(env,
policy,
horizon,
test_batchsize,
action_filter=action_filter,
seed=seed,
njobs=parallel,
deterministic=True,
key=info_key)
log_row['TestPerf'] = performance(test_batch, disc)
log_row['TestInfo'] = mean_sum_info(test_batch).item()
log_row['UTestPerf'] = performance(test_batch, 1)
#Render the agent's behavior
if render and it % render == 0:
generate_batch(env,
policy,
horizon,
episodes=1,
action_filter=action_filter,
render=True,
key=info_key)
#Collect trajectories
batch = generate_batch(env,
policy,
horizon,
batchsize,
action_filter=action_filter,
seed=seed,
n_jobs=parallel,
key=info_key)
log_row['Perf'] = performance(batch, disc)
log_row['Info'] = mean_sum_info(batch).item()
log_row['UPerf'] = performance(batch, disc=1.)
log_row['AvgHorizon'] = avg_horizon(batch)
log_row['Exploration'] = policy.exploration().item()
#Estimate policy gradient
if estimator == 'gpomdp':
grad = gpomdp_estimator(batch,
disc,
policy,
baselinekind=baseline,
shallow=shallow)
elif estimator == 'reinforce':
grad = reinforce_estimator(batch,
disc,
policy,
baselinekind=baseline,
shallow=shallow)
else:
raise ValueError('Invalid policy gradient estimator')
if verbose > 1:
print('Gradients: ', grad)
log_row['GradNorm'] = torch.norm(grad).item()
#Select meta-parameters
stepsize = stepper.next(grad)
log_row['StepSize'] = torch.norm(torch.tensor(stepsize)).item()
#Update policy parameters
new_params = params + stepsize * grad
policy.set_from_flat(new_params)
#Log
log_row['Time'] = time.time() - start
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
if log_grad:
for i in range(policy.num_params()):
log_row['grad%d' % i] = grad[i].item()
logger.write_row(log_row, it)
#Save parameters
if save_params and it % save_params == 0:
logger.save_params(params, it)
#Next iteration
it += 1
#Save final parameters
if save_params:
logger.save_params(params, it)
#Cleanup
logger.close()
def adabatch(env,
policy,
horizon,
pen_coeff,
*,
bound='chebyshev',
var_bound=None,
grad_range=None,
conf=0.2,
min_batchsize=32,
max_batchsize=10000,
iterations=float('inf'),
max_samples=1e6,
disc=0.9,
action_filter=None,
estimator='gpomdp',
baseline='peters',
logger=Logger(name='AdaBatch'),
shallow=True,
meta_conf=0.05,
seed=None,
test_batchsize=False,
info_key='danger',
save_params=100,
log_params=True,
log_grad=False,
parallel=False,
render=False,
verbose=1):
"""
Safe PG algorithm from "Adaptive Batch Size for Safe Policy Gradients",
Papini et al., 2017.
Only for Gaussian policies.
env: environment
policy: the one to improve
horizon: maximum task horizon
pen_coeff: penalty coefficient for policy update
bound: statistical inequality used to determine optimal batchsize
(chebyshev/student/hoeffding/bernstein)
var_bound: upper bound on the variance of the PG estimator. Must not be
None if Chebyshev's bound is employed
grad_range: theoretical range of gradient estimate. If none, it is
estimated from data (in a biased way)
conf: probability of failure
min_batchsize: minimum number of trajectories to estimate policy gradient
max_batchsize: maximum number of trajectories to estimate policy gradient
iterations: number of policy updates
max_samples: maximum number of total trajectories
disc: discount factor
action_filter: function to apply to the agent's action before feeding it to
the environment, not considered in gradient estimation. By default,
the action is clipped to satisfy evironmental boundaries
estimator: either 'reinforce' or 'gpomdp' (default). The latter typically
suffers from less variance
baseline: control variate to be used in the gradient estimator. Either
'avg' (average reward, default), 'peters' (variance-minimizing) or
'zero' (no baseline)
logger: for human-readable logs (standard output, csv, tensorboard...)
shallow: whether to employ pre-computed score functions (only available for
shallow policies)
meta_conf: confidence level of safe-update test (for evaluation only)
seed: random seed (None for random behavior)
test_batchsize: number of test trajectories used to evaluate the
corresponding deterministic policy at each iteration. If 0 or False, no
test is performed
info_key: name of the environment info to log
save_params: how often (every x iterations) to save the policy
parameters to disk. Final parameters are always saved for
x>0. If False, they are never saved.
log_params: whether to include policy parameters in the human-readable logs
log_grad: whether to include gradients in the human-readable logs
parallel: number of parallel jobs for simulation. If 0 or False,
sequential simulation is performed.
render: how often (every x iterations) to render the agent's behavior
on a sample trajectory. If False, no rendering happens
verbose: level of verbosity
"""
#Defaults
if action_filter is None:
action_filter = clip(env)
if bound == 'chebyshev' and var_bound is None:
raise NotImplementedError
empirical_range = (grad_range is None)
#Seed agent
if seed is not None:
seed_all_agent(seed)
#Prepare logger
algo_info = {
'Algorithm': 'AdaBatch',
'Estimator': estimator,
'Baseline': baseline,
'Env': str(env),
'Horizon': horizon,
'Discount': disc,
'Confidence': conf,
'ConfidenceParam': conf,
'Seed': seed,
'MinBatchSize': min_batchsize,
'MaxBatchSize': max_batchsize,
'PenalizationCoefficient': pen_coeff,
'VarianceBound': var_bound,
'Bound': bound
}
logger.write_info({**algo_info, **policy.info()})
log_keys = [
'Perf', 'UPerf', 'AvgHorizon', 'StepSize', 'GradNorm', 'Time',
'StepSize', 'BatchSize', 'Info', 'TotSamples', 'GradVar', 'GradRange',
'Safety', 'Err', 'GradInfNorm'
]
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if log_grad:
log_keys += ['grad%d' % i for i in range(policy.num_params())]
if test_batchsize:
log_keys += ['TestPerf', 'TestPerf', 'TestInfo']
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
#Initializations
it = 0
tot_samples = 0
safety = 1.
optimal_batchsize = min_batchsize
_estimator = (reinforce_estimator
if estimator == 'reinforce' else gpomdp_estimator)
updated = False
updates = 0
unsafe_updates = 0
params = policy.get_flat()
max_grad = torch.zeros_like(params) - float('inf')
min_grad = torch.zeros_like(params) + float('inf')
#Learning loop
while (it < iterations and tot_samples < max_samples):
start = time.time()
if verbose:
print('\n* Iteration %d *' % it)
params = policy.get_flat()
#Test the corresponding deterministic policy
if test_batchsize:
test_batch = generate_batch(env,
policy,
horizon,
episodes=test_batchsize,
action_filter=action_filter,
n_jobs=parallel,
deterministic=True,
key=info_key)
log_row['TestPerf'] = performance(test_batch, disc)
log_row['UTestPerf'] = performance(test_batch, 1)
log_row['TestInfo'] = mean_sum_info(test_batch).item()
#Render the agent's behavior
if render and it % render == 0:
generate_batch(env,
policy,
horizon,
episodes=1,
action_filter=action_filter,
render=True)
#Collect trajectories according to previous optimal batch size
batch = generate_batch(env,
policy,
horizon,
episodes=max(
min_batchsize,
min(max_batchsize, optimal_batchsize)),
action_filter=action_filter,
n_jobs=parallel,
key=info_key)
batchsize = len(batch)
#Estimate policy gradient
grad_samples = _estimator(batch,
disc,
policy,
baselinekind=baseline,
shallow=shallow,
result='samples')
grad = torch.mean(grad_samples, 0)
grad_infnorm = torch.max(torch.abs(grad))
coordinate = torch.min(torch.argmax(torch.abs(grad))).item()
#Compute statistics for estimation error
if bound in ['bernstein', 'student']:
grad_var = torch.var(grad_samples, 0, unbiased=True)
grad_var = torch.max(grad_var).item()
log_row['GradVar'] = grad_var
else:
log_row['GradVar'] = var_bound
if bound in ['bernstein', 'hoeffding'] and empirical_range:
max_grad = torch.max(grad, max_grad)
min_grad = torch.min(min_grad, grad)
grad_range = torch.max(max_grad - min_grad).item()
if grad_range <= 0:
grad_range = torch.max(2 * abs(max_grad)).item()
log_row['GradRange'] = grad_range
#Compute estimation error
if bound == 'chebyshev':
eps = math.sqrt(var_bound / conf)
elif bound == 'student':
quant = sts.t.ppf(1 - conf, batchsize)
eps = quant * math.sqrt(grad_var)
elif bound == 'hoeffding':
eps = grad_range * math.sqrt(math.log(2. / conf) / 2)
elif bound == 'bernstein':
eps = math.sqrt(2 * grad_var * math.log(3. / conf))
eps2 = 3 * grad_range * math.log(3. / conf)
#Compute optimal batch size
if bound in ['chebyshev', 'student', 'hoeffding']:
optimal_batchsize = math.ceil(((13 + 3 * math.sqrt(17)) * eps**2 /
(2 * grad_infnorm**2)).item())
min_safe_batchsize = math.ceil((eps**2 / grad_infnorm**2).item())
else:
min_safe_batchsize = math.ceil(
((eps + math.sqrt(eps**2 + 4 * eps2 * grad_infnorm)) /
(2 * grad_infnorm))**2)
optimal_batchsize = min_safe_batchsize
_stepsize = ((grad_infnorm - eps / math.sqrt(optimal_batchsize) -
eps2 / optimal_batchsize)**2 /
(2 * pen_coeff *
(grad_infnorm + eps / math.sqrt(optimal_batchsize) +
eps2 / optimal_batchsize)**2)).item()
ups = (grad_infnorm**2 * _stepsize * (1 - pen_coeff * _stepsize) /
optimal_batchsize)
old_ups = -float('inf')
while ups > old_ups:
optimal_batchsize += 1
old_ups = ups
_stepsize = (
(grad_infnorm - eps / math.sqrt(optimal_batchsize) -
eps2 / optimal_batchsize)**2 /
(2 * pen_coeff *
(grad_infnorm + eps / math.sqrt(optimal_batchsize) +
eps2 / optimal_batchsize)**2)).item()
ups = (grad_infnorm**2 * _stepsize *
(1 - pen_coeff * _stepsize) / optimal_batchsize)
optimal_batchsize -= 1
if verbose:
print('Optimal batch size: %d' % optimal_batchsize)
#Update long-term quantities
tot_samples += batchsize
#Update safety measure
if updates == 0:
old_rets = returns(batch, disc)
elif updated:
new_rets = returns(batch, disc)
tscore, pval = sts.ttest_ind(old_rets, new_rets)
if pval / 2 < meta_conf and tscore > 0:
unsafe_updates += 1
if verbose:
print('The previous update was unsafe! (p-value = %f)' %
(pval / 2))
old_rets = new_rets
safety = 1 - unsafe_updates / updates
#Log
log_row['Err'] = eps
log_row['Safety'] = safety
log_row['Perf'] = performance(batch, disc)
log_row['Info'] = mean_sum_info(batch).item()
log_row['UPerf'] = performance(batch, disc=1.)
log_row['AvgHorizon'] = avg_horizon(batch)
log_row['GradNorm'] = torch.norm(grad).item()
log_row['GradInfNorm'] = grad_infnorm.item()
log_row['BatchSize'] = batchsize
log_row['TotSamples'] = tot_samples
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
if log_grad:
for i in range(policy.num_params()):
log_row['grad%d' % i] = grad[i].item()
#Check if number of samples is sufficient to perform update
if grad_infnorm < eps / math.sqrt(batchsize):
updated = False
if verbose:
print('No update, need more samples')
#Log
log_row['StepSize'] = 0.
log_row['Time'] = time.time() - start
if verbose:
print(separator)
logger.write_row(log_row, it)
if verbose:
print(separator)
#Skip to next iteration (current trajectories are discarded)
it += 1
continue
#Select step size
if bound == 'bernstein':
stepsize = ((grad_infnorm - eps / math.sqrt(batchsize) -
eps2 / batchsize)**2 /
(2 * pen_coeff *
(grad_infnorm + eps / math.sqrt(batchsize) +
eps2 / batchsize)**2)).item()
else:
stepsize = (13 - 3 * math.sqrt(17)) / (4 * pen_coeff)
log_row['StepSize'] = stepsize
#Update policy parameters
new_params = params
new_params[coordinate] = (params[coordinate] +
stepsize * grad[coordinate])
policy.set_from_flat(new_params)
updated = True
updates += 1
#Save parameters
if save_params and it % save_params == 0:
logger.save_params(params, it)
#Next iteration
log_row['Time'] = time.time() - start
if verbose:
print(separator)
logger.write_row(log_row, it)
if verbose:
print(separator)
it += 1
#Save final parameters
if save_params:
logger.save_params(params, it)
#Cleanup
logger.close()
def adastep(env,
policy,
horizon,
pen_coeff,
var_bound,
*,
conf=0.2,
batchsize=5000,
iterations=float('inf'),
max_samples=1e6,
disc=0.9,
action_filter=None,
estimator='gpomdp',
baseline='peters',
logger=Logger(name='AdaStep'),
shallow=True,
meta_conf=0.05,
seed=None,
test_batchsize=False,
info_key='danger',
save_params=100,
log_params=True,
log_grad=False,
parallel=False,
render=False,
verbose=1):
"""
Safe PG algorithm from "Adaptive Step Size for Policy Gradient Methods",
Pirotta et al., 2013.
Only for Gaussian policies.
env: environment
policy: the one to improve
horizon: maximum task horizon
pen_coeff: penalty coefficient for policy update
var_bound: upper bound on the variance of the PG estimator
conf: probability of failure
batchsize: number of trajectories to estimate policy gradient
iterations: maximum number of learning iterations
max_samples: maximum number of total trajectories
disc: discount factor
action_filter: function to apply to the agent's action before feeding it to
the environment, not considered in gradient estimation. By default,
the action is clipped to satisfy evironmental boundaries
estimator: either 'reinforce' or 'gpomdp' (default). The latter typically
suffers from less variance
baseline: control variate to be used in the gradient estimator. Either
'avg' (average reward, default), 'peters' (variance-minimizing) or
'zero' (no baseline)
logger: for human-readable logs (standard output, csv, tensorboard...)
shallow: whether to employ pre-computed score functions (only available for
shallow policies)
meta_conf: confidence level of safe-update test (for evaluation only)
seed: random seed (None for random behavior)
test_batchsize: number of test trajectories used to evaluate the
corresponding deterministic policy at each iteration. If 0 or False, no
test is performed
info_key: name of the environment info to log
save_params: how often (every x iterations) to save the policy
parameters to disk. Final parameters are always saved for
x>0. If False, they are never saved.
log_params: whether to include policy parameters in the human-readable logs
log_grad: whether to include gradients in the human-readable logs
parallel: number of parallel jobs for simulation. If 0 or False,
sequential simulation is performed.
render: how often (every x iterations) to render the agent's behavior
on a sample trajectory. If False, no rendering happens
verbose: level of verbosity on standard output
"""
#Defaults
if action_filter is None:
action_filter = clip(env)
#Seed agent
if seed is not None:
seed_all_agent(seed)
#Prepare logger
algo_info = {
'Algorithm': 'AdaStep',
'Estimator': estimator,
'Baseline': baseline,
'Env': str(env),
'Horizon': horizon,
'Discount': disc,
'Confidence': conf,
'ConfidenceParam': conf,
'Seed': seed,
'BatchSize': batchsize,
'PenalizationCoefficient': pen_coeff,
'VarianceBound': var_bound
}
logger.write_info({**algo_info, **policy.info()})
log_keys = [
'Perf', 'UPerf', 'AvgHorizon', 'StepSize', 'GradNorm', 'Time',
'StepSize', 'BatchSize', 'Info', 'TotSamples', 'Safety'
]
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if log_grad:
log_keys += ['grad%d' % i for i in range(policy.num_params())]
if test_batchsize:
log_keys += ['TestPerf', 'TestPerf', 'TestInfo']
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
#Initializations
it = 0
tot_samples = 0
safety = 1.
_estimator = (reinforce_estimator
if estimator == 'reinforce' else gpomdp_estimator)
updated = False
updates = 0
unsafe_updates = 0
eps = math.sqrt(var_bound / conf)
#Learning loop
while (it < iterations and tot_samples < max_samples):
start = time.time()
if verbose:
print('\n* Iteration %d *' % it)
params = policy.get_flat()
#Test the corresponding deterministic policy
if test_batchsize:
test_batch = generate_batch(env,
policy,
horizon,
episodes=test_batchsize,
action_filter=action_filter,
n_jobs=parallel,
deterministic=True,
key=info_key)
log_row['TestPerf'] = performance(test_batch, disc)
log_row['UTestPerf'] = performance(test_batch, 1)
log_row['TestInfo'] = mean_sum_info(test_batch).item()
#Render the agent's behavior
if render and it % render == 0:
generate_batch(env,
policy,
horizon,
episodes=1,
action_filter=action_filter,
render=True)
#Collect trajectories according to fixed batch size
batch = generate_batch(env,
policy,
horizon,
episodes=batchsize,
action_filter=action_filter,
n_jobs=parallel,
key=info_key)
#Estimate policy gradient
grad_samples = _estimator(batch,
disc,
policy,
baselinekind=baseline,
shallow=shallow,
result='samples')
grad = torch.mean(grad_samples, 0)
lower = torch.clamp(
torch.abs(grad) - eps / math.sqrt(batchsize), 0, float('inf'))
upper = torch.abs(grad) + eps / math.sqrt(batchsize)
#Update long-term quantities
tot_samples += batchsize
#Update safety measure
if updates == 0:
old_rets = returns(batch, disc)
elif updated:
new_rets = returns(batch, disc)
tscore, pval = sts.ttest_ind(old_rets, new_rets)
if pval / 2 < meta_conf and tscore > 0:
unsafe_updates += 1
if verbose:
print('The previous update was unsafe! (p-value = %f)' %
(pval / 2))
old_rets = new_rets
safety = 1 - unsafe_updates / updates
#Log
log_row['Safety'] = safety
log_row['Perf'] = performance(batch, disc)
log_row['Info'] = mean_sum_info(batch).item()
log_row['UPerf'] = performance(batch, disc=1.)
log_row['AvgHorizon'] = avg_horizon(batch)
log_row['GradNorm'] = torch.norm(grad).item()
log_row['BatchSize'] = batchsize
log_row['TotSamples'] = tot_samples
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
if log_grad:
for i in range(policy.num_params()):
log_row['grad%d' % i] = grad[i].item()
#Check if number of samples is sufficient to perform update
if torch.norm(lower) == 0:
updated = False
if verbose:
print('No update, would require more samples')
#Select step size
stepsize = (torch.norm(lower)**2 /
(2 * pen_coeff * torch.sum(upper)**2)).item()
log_row['StepSize'] = stepsize
#Update policy parameters
new_params = params + stepsize * grad
policy.set_from_flat(new_params)
updated = True
updates += 1
#Save parameters
if save_params and it % save_params == 0:
logger.save_params(params, it)
#Next iteration
log_row['Time'] = time.time() - start
if verbose:
print(separator)
logger.write_row(log_row, it)
if verbose:
print(separator)
it += 1
#Save final parameters
if save_params:
logger.save_params(params, it)
#Cleanup
logger.close()
d = sum(env.action_space.shape)
mu_init = torch.zeros(m)
logstd_init = torch.log(torch.zeros(1) + args.sigmainit)
policy = ShallowGaussianPolicy(m,
d,
mu_init=mu_init,
logstd_init=logstd_init,
learn_std=True)
if args.safety == 'mi':
safety_req = MonotonicImprovement(0.)
elif args.safety == 'budget':
from potion.common.misc_utils import seed_all_agent
seed_all_agent(args.seed)
env.seed(args.seed)
batch = generate_batch(env, policy, args.horizon, args.batchsize)
perf = performance(batch, args.gamma)
safety_req = Budget(initial_perf=perf)
else:
safety_req = FixedThreshold(threshold=float(args.safety))
env.seed(args.seed)
test_batchsize = 100 if args.test else 0
envname = re.sub(r'[^a-zA-Z]', "", args.env)[:-1]
envname = re.sub(r'[^a-zA-Z]', "", args.env)[:-1].lower()
logname = envname + '_' + args.name + '_' + str(args.seed)
if args.temp:
logger = Logger(directory='../temp', name=logname)
"""Testing"""
if __name__ == '__main__':
from potion.actors.continuous_policies import ShallowGaussianPolicy as Gauss
from potion.simulation.trajectory_generators import generate_batch
from potion.common.misc_utils import seed_all_agent
import potion.envs
import gym.spaces
env = gym.make('ContCartPole-v0')
env.seed(0)
seed_all_agent(0)
N = 100
H = 100
disc = 0.99
pol = Gauss(4,1, mu_init=[0.,0.,0.,0.], learn_std=True)
batch = generate_batch(env, pol, H, N)
o = gpomdp_estimator(batch, disc, pol, baselinekind='peters',
shallow=True)
print('Shallow GPOMDP (peters):', o)
#o = gpomdp_estimator(batch, disc, pol, baselinekind='peters')
#print('GPOMDP (peters)', o)
#print()
print('Cum version')
cum_1 = cum_2 = cum_3 = 0.
i = 0
for t in batch:
i+=1
cum_1, cum_2, cum3 = _incr_shallow_gpomdp_estimator(t, disc, pol, 'peters', 'mean', cum_1, cum_2, cum_3, i)
#print(cum_1, cum_2, cum_3)
def mepg(env,
policy,
horizon,
batchsize=500,
iterations=200,
disc=0.99,
alpha=1e-1,
eta=1e-3,
clip_at=100,
test_batchsize=False,
render=False,
seed=None,
action_filter=None,
parallel=False,
logger=Logger(name='MEPG'),
save_params=50,
log_params=True,
verbose=True):
"""
MEPG algorithm
Only for shallow Gaussian policy w/ scalar variance
"""
#Defaults
assert type(policy) == ShallowGaussianPolicy
assert policy.learn_std
if action_filter is None:
action_filter = clip(env)
#Seed agent
if seed is not None:
seed_all_agent(seed)
#Prepare logger
algo_info = {
'Algorithm': 'MEPG',
'Environment': str(env),
'BatchSize': batchsize,
'Horizon': horizon,
'Iterations': iterations,
'Disc': disc,
'Alpha': alpha,
'Eta': eta,
'Seed': seed,
'ActionFilter': action_filter
}
logger.write_info({**algo_info, **policy.info()})
log_keys = [
'Perf', 'UPerf', 'AvgHorizon', 'StepSize', 'MetaStepSize', 'BatchSize',
'Exploration', 'OmegaGrad', 'OmegaMetagrad', 'upsilonGradNorm'
]
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if test_batchsize:
log_keys.append('DetPerf')
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
#Learning loop
it = 0
while (it < iterations):
#Begin iteration
if verbose:
print('\nIteration ', it)
if verbose:
print('Params: ', policy.get_flat())
#Test mean parameters on deterministic policy
if test_batchsize:
test_batch = generate_batch(env,
policy,
horizon,
test_batchsize,
action_filter=action_filter,
seed=seed,
njobs=parallel,
deterministic=True)
log_row['DetPerf'] = performance(test_batch, disc)
#Render behavior
if render:
generate_batch(env, policy, horizon, 1, action_filter, render=True)
#Set metaparameters
omega = policy.get_scale_params()
sigma = torch.exp(omega)
stepsize = alpha * sigma**2
#Collect trajectories
batch = generate_batch(env,
policy,
horizon,
batchsize,
action_filter=action_filter,
seed=seed,
n_jobs=parallel)
#Estimate policy gradient
grad = gpomdp_estimator(batch,
disc,
policy,
baselinekind='peters',
shallow=True)
upsilon_grad = grad[1:]
omega_grad = grad[0]
omega_metagrad = metagrad(batch,
disc,
policy,
alpha,
clip_at,
grad=grad)
upsilon = policy.get_loc_params()
new_upsilon = upsilon + stepsize * upsilon_grad
policy.set_loc_params(new_upsilon)
new_omega = omega + eta * omega_metagrad
policy.set_scale_params(new_omega)
# Log
log_row['Exploration'] = policy.exploration()
log_row['StepSize'] = stepsize.item()
log_row['MetaStepSize'] = eta
log_row['OmegaGrad'] = omega_grad.item()
log_row['OmegaMetagrad'] = omega_metagrad.item()
log_row['UpsilonGradNorm'] = torch.norm(upsilon_grad).item()
log_row['BatchSize'] = batchsize
log_row['Perf'] = performance(batch, disc)
log_row['UPerf'] = performance(batch, 1.)
log_row['AvgHorizon'] = avg_horizon(batch)
params = policy.get_flat()
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
logger.write_row(log_row, it)
if save_params and it % save_params == 0:
logger.save_params(params, it)
# Next iteration
it += 1
# Final policy
if save_params:
logger.save_params(params, it)
def ssepg(env,
policy,
horizon,
batchsize=100,
iterations=200,
disc=0.99,
pow_alpha=0.01,
pow_err_tol=0.1,
max_pow_it=100,
max_pow_attempts=3,
pow_clip=0.2,
safety_req=MonotonicImprovement(0.),
conf=0.2,
adapt_batchsize=False,
test_batchsize=False,
render=False,
seed=None,
action_filter=None,
parallel=False,
logger=Logger(name='SEPG'),
save_params=50,
log_params=True,
verbose=True):
"""
SSEPG algorithm
Only for shallow Gaussian policy w/ scalar variance
"""
#Defaults
assert type(policy) == ShallowGaussianPolicy
assert policy.learn_std
if action_filter is None:
action_filter = clip(env)
#Seed agent
if seed is not None:
seed_all_agent(seed)
#Prepare logger
algo_info = {
'Algorithm': 'MEPG',
'Environment': str(env),
'BatchSize': batchsize,
'Horizon': horizon,
'Iterations': iterations,
'Disc': disc,
'Seed': seed,
'ActionFilter': action_filter
}
logger.write_info({**algo_info, **policy.info()})
log_keys = [
'Perf', 'UPerf', 'AvgHorizon', 'StepSize', 'MetaStepSize', 'BatchSize',
'Exploration', 'OmegaGrad', 'OmegaMetagrad', 'UpsilonGradNorm',
'UpsilonGradVar', 'UpsilonEps', 'OmegaGradVar', 'OmegaEps', 'Req',
'MinBatchSize', 'MaxReq', 'UpsilonLip', 'OmegaLip'
]
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if test_batchsize:
log_keys.append('DetPerf')
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
#Learning loop
it = 0
stepsize = 0.
metastepsize = 0.
omega_grad_var = 0.
omega_eps = 0.
omega_metagrad = torch.zeros(1)
G = 0.
while (it < iterations):
#Begin iteration
if verbose:
print('\nIteration ', it)
if verbose:
print('Params: ', policy.get_flat())
#Test mean parameters on deterministic policy
if test_batchsize:
test_batch = generate_batch(env,
policy,
horizon,
test_batchsize,
action_filter=action_filter,
seed=seed,
njobs=parallel,
deterministic=True)
log_row['DetPerf'] = performance(test_batch, disc)
#Render behavior
if render:
generate_batch(env, policy, horizon, 1, action_filter, render=True)
#Set metaparameters
omega = policy.get_scale_params()
sigma = torch.exp(omega)
#Collect trajectories
batch = generate_batch(env,
policy,
horizon,
batchsize,
action_filter=action_filter,
seed=seed,
n_jobs=parallel)
perf = performance(batch, disc)
#Estimate policy gradient
grad_samples = gpomdp_estimator(batch,
disc,
policy,
baselinekind='peters',
shallow=True,
result='samples')
grad = torch.mean(grad_samples, 0)
upsilon_grad = grad[1:]
upsilon_grad_norm = torch.norm(upsilon_grad)
omega_grad = grad[0]
dfn = upsilon_grad.shape[0]
### Mean-update iteration
if it % 2 == 0:
#Compute gradient estimation error for mean parameters
if conf < 1 and grad_samples.size()[1] > 2:
centered = grad_samples[:, 1:] - upsilon_grad.unsqueeze(0)
grad_cov = batchsize / (batchsize - 1) * torch.mean(
torch.bmm(centered.unsqueeze(2), centered.unsqueeze(1)), 0)
upsilon_grad_var = torch.sum(torch.diag(grad_cov)).item()
max_eigv = eigsh(grad_cov.numpy(), 1)[0][0]
quant = sts.f.ppf(1 - conf, dfn, batchsize - dfn)
upsilon_eps = math.sqrt(max_eigv * dfn * quant)
elif conf < 1:
upsilon_grad_var = torch.var(grad_samples[:, 1]).item()
quant = sts.t.ppf(1 - conf / 2, batchsize - 1)
upsilon_eps = quant * math.sqrt(upsilon_grad_var)
else:
upsilon_eps = 0.
upsilon_grad_var = 0.
#Compute safe step size for mean parameters
mask = torch.ones_like(grad)
mask[0] = 0.
F = power(policy,
batch,
grad,
disc,
pow_alpha=pow_alpha,
err_tol=pow_err_tol,
max_it=max_pow_it,
max_attempts=max_pow_attempts,
shallow=True,
clip=pow_clip,
verbose=verbose,
mask=mask)
req = safety_req.next(perf)
max_req = (upsilon_grad_norm - upsilon_eps / math.sqrt((batchsize - dfn)))**2 / \
(2 * F)
req = min(req, max_req)
alpha = (upsilon_grad_norm - upsilon_eps / math.sqrt((batchsize - dfn))) / \
F * \
(1 + math.sqrt(1 - req / max_req))
stepsize = (alpha / upsilon_grad_norm).item()
#Ensure minimum safe batchsize
min_batchsize = math.ceil(
(upsilon_eps**2 / upsilon_grad_norm**2).item() + 1e-12) + dfn
if conf < 1 and adapt_batchsize:
batchsize = max(batchsize, min_batchsize)
#Update mean parameters
upsilon = policy.get_loc_params()
new_upsilon = upsilon + alpha * sigma**2 * upsilon_grad / upsilon_grad_norm
policy.set_loc_params(new_upsilon)
###
### Variance-update iteration
else:
#Estimate meta gradient (alpha from previous step)
omega_metagrad = metagrad(batch,
disc,
policy,
alpha,
grad_samples=grad_samples)
omega_metagrad_norm = torch.norm(omega_metagrad)
#Compute gradient estimation error for variance parameter
if conf < 1:
omega_grad_var = torch.var(grad_samples[:, 0]).item()
quant = sts.t.ppf(1 - conf / 2, batchsize - 1)
omega_eps = quant * math.sqrt(omega_grad_var / batchsize)
else:
omega_grad_var = 0.
omega_eps = 0.
#Compute safe meta step size
mask = torch.zeros_like(grad)
mask[0] = 1.
G = power(policy,
batch,
grad,
disc,
pow_alpha=pow_alpha,
err_tol=pow_err_tol,
max_it=max_pow_it,
max_attempts=max_pow_attempts,
shallow=True,
clip=pow_clip,
verbose=verbose,
mask=mask)
req = safety_req.next(perf)
proj = omega_grad.view(-1).dot(
omega_metagrad.view(-1)) / torch.norm(omega_metagrad)
max_req = (torch.abs(proj) -
omega_eps / math.sqrt(batchsize))**2 / (2 * G)
req = min(req, max_req)
eta = (torch.abs(proj) - omega_eps / math.sqrt(batchsize)) / \
G * \
(torch.sign(proj) + torch.sqrt(1 - req / max_req))
metastepsize = (eta / omega_metagrad_norm).item()
#Ensure minimum safe batchsize
min_batchsize = math.ceil((omega_eps**2 / proj**2).item() + 1e-12)
if conf < 1 and adapt_batchsize:
batchsize = max(batchsize, min_batchsize)
#Update variance parameters
new_omega = omega + eta * omega_metagrad / omega_metagrad_norm
policy.set_scale_params(new_omega)
# Log
log_row['UpsilonLip'] = F
log_row['OmegaLip'] = G
log_row['Exploration'] = sigma.item()
log_row['StepSize'] = stepsize
log_row['MetaStepSize'] = metastepsize
log_row['OmegaGrad'] = omega_grad.item()
log_row['OmegaMetagrad'] = omega_metagrad.item()
log_row['UpsilonGradNorm'] = torch.norm(upsilon_grad).item()
log_row['BatchSize'] = batchsize
log_row['Perf'] = perf
log_row['UPerf'] = performance(batch, 1.)
log_row['AvgHorizon'] = avg_horizon(batch)
log_row['UpsilonGradVar'] = upsilon_grad_var
log_row['UpsilonEps'] = upsilon_eps
log_row['OmegaGradVar'] = upsilon_grad_var
log_row['OmegaEps'] = upsilon_eps
log_row['Req'] = req
print(min_batchsize)
log_row['MinBatchSize'] = min_batchsize
log_row['MaxReq'] = max_req.item()
params = policy.get_flat()
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
logger.write_row(log_row, it)
if save_params and it % save_params == 0:
logger.save_params(params, it)
# Next iteration
it += 1
# Final policy
if save_params:
logger.save_params(params, it)
max_rew = env.Q * env.max_pos**2 + env.R * env.max_action**2
seed = 0
env.seed(seed)
seed_all_agent(seed)
pol = ShallowGaussianPolicy(1, 1, learn_std=False, logstd_init=np.log(std))
estimated = []
bound = []
real = []
fo = []
op = []
params = np.linspace(-1., 0., points)
it = 0
for param in params:
plt.close()
it+=1
print(it)
pol.set_from_flat([param])
batch = generate_batch(env, pol, horizon, batchsize)
grad = gpomdp_estimator(batch, disc, pol, shallow=True)
estimated.append(power(pol, batch, grad, disc, step=0.01, max_it=100, clip=0.1))
real.append(abs(env._hess(param, std, disc, horizon=horizon)))
#bound.append(gauss_lip_const(max_feat, max_rew, disc, std))
plt.plot(params, estimated, label='Estimated')
plt.plot(params, real, label='True')
#plt.plot(params, bound, label='Bound')
plt.legend()
plt.show()
def semisafepg(env, policy, horizon, *,
conf = 0.05,
min_batchsize = 32,
max_batchsize = 5000,
iterations = float('inf'),
max_samples = 1e6,
disc = 0.9,
forget = 0.1,
action_filter = None,
estimator = 'gpomdp',
baseline = 'peters',
logger = Logger(name='SSPG'),
shallow = True,
pow_step = 0.01,
pow_decay = 0.99,
pow_it = 100,
pow_tol = 0.05,
pow_clip = 0.1,
fast = False,
meta_conf = 0.05,
seed = None,
test_batchsize = False,
info_key = 'danger',
save_params = 100,
log_params = True,
log_grad = False,
parallel = False,
render = False,
verbose = 1):
"""
Semi-safe PG algorithm from "Smoothing Policies and Safe Policy Gradients,
Papini et al., 2019
env: environment
policy: the one to improve
horizon: maximum task horizon
conf: probability of unsafety (per update)
min_batchsize: minimum number of trajectories used to estimate policy
gradient
max_batchsize: maximum number of trajectories used to estimate policy
gradient
iterations: maximum number of learning iterations
max_samples: maximum number of total trajectories
disc: discount factor
forget: decay of the (estimated) global gradient Lipscthiz constant
action_filter: function to apply to the agent's action before feeding it to
the environment, not considered in gradient estimation. By default,
the action is clipped to satisfy evironmental boundaries
estimator: either 'reinforce' or 'gpomdp' (default). The latter typically
suffers from less variance
baseline: control variate to be used in the gradient estimator. Either
'avg' (average reward, default), 'peters' (variance-minimizing) or
'zero' (no baseline)
logger: for human-readable logs (standard output, csv, tensorboard)
shallow: whether to employ pre-computed score functions (only available for
shallow policies)
pow_step: step size of the power method
pow_decay: initial decay parameter of the power method
pow_it: maximum number of iterations (per epoch) of the power method
pow_tol: relative-error tolerance of the power method
pow_clip: importance-weight clipping parameter for the power method
(default 0.2)
fast: whether to pursue maximum convergence speed
(under safety constraints)
meta_conf: confidence level of safe update test (for evaluation)
seed: random seed (None for random behavior)
test_batchsize: number of test trajectories used to evaluate the
corresponding deterministic policy at each iteration. If False, no
test is performed
info_key: name of the environment info to log
save_params: how often (every x iterations) to save the policy
parameters to disk. Final parameters are always saved for
x>0. If False, they are never saved.
log_params: whether to include policy parameters in the human-readable logs
log_grad: whether to include gradients in the human-readable logs
parallel: number of parallel jobs for simulation. If False,
sequential simulation is performed.
render: how often (every x iterations) to render the agent's behavior
on a sample trajectory. If False, no rendering happens
verbose: level of verbosity
"""
#Defaults
if action_filter is None:
action_filter = clip(env)
#Seed agent
if seed is not None:
seed_all_agent(seed)
#Prepare logger
algo_info = {'Algorithm': 'SSPG',
'Estimator': estimator,
'Baseline': baseline,
'Env': str(env),
'Horizon': horizon,
'Discount': disc,
'Confidence': conf,
'ConfidenceParam': conf,
'Seed': seed,
'MinBatchSize': min_batchsize,
'MaxBatchSize': max_batchsize,
'ForgetParam': forget,
'PowerStep': pow_step,
'PowerDecay': pow_decay,
'PowerIters': pow_it,
'PowerTolerance': pow_tol,
'Fast': fast
}
logger.write_info({**algo_info, **policy.info()})
log_keys = ['Perf',
'UPerf',
'AvgHorizon',
'StepSize',
'GradNorm',
'Time',
'StepSize',
'BatchSize',
'LipConst',
'ErrBound',
'SampleVar',
'Info',
'TotSamples',
'Safety',
'UScore']
if log_params:
log_keys += ['param%d' % i for i in range(policy.num_params())]
if log_grad:
log_keys += ['grad%d' % i for i in range(policy.num_params())]
if test_batchsize:
log_keys += ['TestPerf', 'TestPerf', 'TestInfo']
log_row = dict.fromkeys(log_keys)
logger.open(log_row.keys())
#Initializations
it = 0
updated = False
updates = 0
unsafe_updates = 0
safety = 1.
tot_samples = 0
optimal_batchsize = min_batchsize
min_safe_batchsize = min_batchsize
_conf = conf
_estimator = (reinforce_estimator
if estimator=='reinforce' else gpomdp_estimator)
old_lip_const = 0.
dfn = policy.get_flat().shape[0]
min_batchsize = max(min_batchsize, dfn + 1)
#Learning loop
while(it < iterations and tot_samples < max_samples):
start = time.time()
if verbose:
print('\n* Iteration %d *' % it)
params = policy.get_flat()
#Test the corresponding deterministic policy
if test_batchsize:
test_batch = generate_batch(env, policy, horizon,
episodes=test_batchsize,
action_filter=action_filter,
n_jobs=parallel,
deterministic=True,
key=info_key)
log_row['TestPerf'] = performance(test_batch, disc)
log_row['UTestPerf'] = performance(test_batch, 1)
log_row['TestInfo'] = mean_sum_info(test_batch).item()
#Render the agent's behavior
if render and it % render==0:
generate_batch(env, policy, horizon,
episodes=1,
action_filter=action_filter,
render=True)
#Collect trajectories according to target batch size
target_batchsize = min_safe_batchsize if fast else optimal_batchsize
batch = generate_batch(env, policy, horizon,
episodes=max(min_batchsize,
min(max_batchsize,
target_batchsize)),
action_filter=action_filter,
n_jobs=parallel,
key=info_key)
batchsize = len(batch)
#Collect more trajectories to match minimum safe batch size
do = True
while do or batchsize < min_safe_batchsize:
do = False
batch += generate_batch(env, policy, horizon,
episodes=(min(max_batchsize, min_safe_batchsize)
- batchsize),
action_filter=action_filter,
n_jobs=parallel,
key=info_key)
batchsize = len(batch)
#Estimate policy gradient
grad_samples = _estimator(batch, disc, policy,
baselinekind=baseline,
shallow=shallow,
result='samples')
grad = torch.mean(grad_samples, 0)
#Compute estimation error with ellipsoid confidence region
centered = grad_samples - grad.unsqueeze(0)
grad_cov = (batchsize/(batchsize - 1) *
torch.mean(torch.bmm(centered.unsqueeze(2),
centered.unsqueeze(1)),0))
grad_var = torch.sum(torch.diag(grad_cov)).item() #for humans
max_eigv = eigsh(grad_cov.numpy(), 1)[0][0]
quant = sts.f.ppf(1 - _conf, dfn, batchsize - dfn)
eps = math.sqrt(max_eigv * dfn * quant)
#Optimal batch size
optimal_batchsize = torch.ceil(4 * eps**2 /
(torch.norm(grad)**2) + dfn).item()
min_safe_batchsize = torch.ceil(eps**2 /
torch.norm(grad)**2 + dfn).item()
target_batchsize = (min_safe_batchsize if fast
else optimal_batchsize)
if verbose and optimal_batchsize < max_batchsize:
print('Collected %d / %d trajectories'
% (batchsize, target_batchsize))
elif verbose:
print('Collected %d / %d trajectories'
% (batchsize, min(max_batchsize, target_batchsize)))
#Adjust confidence before collecting more data for the same update
if batchsize >= max_batchsize:
break
_conf /= 2
if verbose:
print('Optimal batch size: %d'
% (optimal_batchsize if optimal_batchsize < float('inf')
else -1))
print('Minimum safe batch size: %d'
% (min_safe_batchsize if min_safe_batchsize < float('inf')
else -1))
if (batchsize >= min_safe_batchsize
and batchsize < optimal_batchsize):
print('Low sample regime')
#Update safety measure
if updates == 0:
old_rets= returns(batch, disc)
elif updated:
new_rets = returns(batch, disc)
tscore, pval = sts.ttest_ind(old_rets, new_rets)
if pval / 2 < meta_conf and tscore > 0:
unsafe_updates += 1
if verbose:
print('The previous update was unsafe! (p-value = %f)'
% (pval / 2))
old_rets = new_rets
safety = 1 - unsafe_updates / updates
#Update long-term quantities
tot_samples += batchsize
#Log
log_row['SampleVar'] = grad_var
log_row['UScore'] = torch.norm(grad).item() / math.sqrt(grad_var)
log_row['Safety'] = safety
log_row['ErrBound'] = eps
log_row['Perf'] = performance(batch, disc)
log_row['Info'] = mean_sum_info(batch).item()
log_row['UPerf'] = performance(batch, disc=1.)
log_row['AvgHorizon'] = avg_horizon(batch)
log_row['GradNorm'] = torch.norm(grad).item()
log_row['BatchSize'] = batchsize
log_row['TotSamples'] = tot_samples
if log_params:
for i in range(policy.num_params()):
log_row['param%d' % i] = params[i].item()
if log_grad:
for i in range(policy.num_params()):
log_row['grad%d' % i] = grad[i].item()
#Check if number of samples is sufficient to perform update
if batchsize < min_safe_batchsize:
updated = False
if verbose:
print('No update, would require more samples than allowed')
#Log
log_row['LipConst'] = old_lip_const
log_row['StepSize'] = 0.
log_row['Time'] = time.time() - start
if verbose:
print(separator)
logger.write_row(log_row, it)
if verbose:
print(separator)
#Adjust confidence before collecting new data for the same update
_conf /= 2
#Skip to next iteration (current trajectories are discarded)
it += 1
continue
#Reset confidence for next update
_conf = conf
#Estimate gradient Lipschitz constant with off-policy Power Method
lip_const = power(policy, batch, grad, disc,
step=pow_step,
decay_rate=pow_decay,
tol=pow_tol,
max_it=pow_it,
estimator=_estimator,
baseline=baseline,
shallow=shallow,
clip=pow_clip,
verbose=verbose)
#Update "global" lipschitz constant
if it > 0:
lip_const = (1 - forget) * max(lip_const, old_lip_const) + forget * lip_const
old_lip_const = lip_const
log_row['LipConst'] = lip_const
#Select step size
stepsize = 1. / lip_const * (1 - eps / (torch.norm(grad)
* math.sqrt(batchsize - dfn)).item())
if fast:
stepsize *= 2
log_row['StepSize'] = stepsize
#Update policy parameters
new_params = params + stepsize * grad
policy.set_from_flat(new_params)
updated = True
updates += 1
#Save parameters
if save_params and it % save_params == 0:
logger.save_params(params, it)
#Next iteration
log_row['Time'] = time.time() - start
if verbose:
print(separator)
logger.write_row(log_row, it)
if verbose:
print(separator)
it += 1
#Save final parameters
if save_params:
logger.save_params(params, it)
#Cleanup
logger.close()