def main(arguments):
# load the features of the dataset
features = datasets.load_breast_cancer().data
# standardize the features
features = StandardScaler().fit_transform(features)
# get the number of features
num_features = features.shape[1]
# load the labels for the features
labels = datasets.load_breast_cancer().target
train_features, test_features, train_labels, test_labels = train_test_split(
features, labels, test_size=0.30, stratify=labels)
model = MLP(alpha=LEARNING_RATE,
batch_size=BATCH_SIZE,
node_size=NUM_NODES,
num_classes=NUM_CLASSES,
num_features=num_features)
model.train(num_epochs=arguments.num_epochs,
log_path=arguments.log_path,
train_data=[train_features, train_labels],
train_size=train_features.shape[0],
test_data=[test_features, test_labels],
test_size=test_features.shape[0],
result_path=arguments.result_path)
def main(arguments):
# load the features of the dataset
features = datasets.load_breast_cancer().data
# standardize the features
features = StandardScaler().fit_transform(features)
# get the number of features
num_features = features.shape[1]
# load the labels for the features
labels = datasets.load_breast_cancer().target
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.30,
stratify=labels)
model = MLP(alpha=LEARNING_RATE, batch_size=BATCH_SIZE, node_size=NUM_NODES, num_classes=NUM_CLASSES,
num_features=num_features)
model.train(num_epochs=arguments.num_epochs, log_path=arguments.log_path, train_data=[train_features, train_labels],
train_size=train_features.shape[0], test_data=[test_features, test_labels],
test_size=test_features.shape[0], result_path=arguments.result_path)
class BCAgent(POLOAgent):
"""
An agent extending upon POLO that uses behavior cloning on the planner
predicted actions as a prior to MPC.
"""
def __init__(self, params):
super(BCAgent, self).__init__(params)
# Initialize policy network
pol_params = self.params['p-bc']['pol_params']
pol_params['input_size'] = self.N
pol_params['output_size'] = self.M
if 'final_activation' not in pol_params:
pol_params['final_activation'] = torch.tanh
self.pol = MLP(pol_params)
# Create policy optimizer
ppar = self.params['p-bc']['pol_optim']
self.pol_optim = torch.optim.Adam(self.pol.parameters(),
lr=ppar['lr'],
weight_decay=ppar['reg'])
# Use a replay buffer that will save planner actions
self.pol_buf = ReplayBuffer(self.N, self.M,
self.params['p-bc']['buf_size'])
# Logging (store cum_rew, cum_emp_rew)
self.hist['pols'] = np.zeros((self.T, 2))
self.has_pol = True
self.pol_cache = ()
def get_action(self):
"""
BCAgent generates a planned trajectory using the behavior-cloned policy
and then optimizes it via MPC.
"""
self.pol.eval()
# Run a rollout using the policy starting from the current state
infos = self.get_traj_info()
self.hist['pols'][self.time] = infos[3:5]
self.pol_cache = (infos[0], infos[2])
self.prior_actions = infos[1]
# Generate trajectory via MPC with the prior actions as a prior
action = super(BCAgent, self).get_action(prior=self.prior_actions)
# Add final planning trajectory to BC buffer
fin_states, fin_rews = self.cache[2], self.cache[3]
fin_states = np.concatenate(([self.prev_obs], fin_states[1:]))
pb_pct = self.params['p-bc']['pb_pct']
pb_len = int(pb_pct * fin_states.shape[0])
for t in range(pb_len):
self.pol_buf.update(fin_states[t], fin_states[t + 1], fin_rews[t],
self.planned_actions[t], False)
return action
def do_updates(self):
"""
Learn from the saved buffer of planned actions.
"""
super(BCAgent, self).do_updates()
if self.time % self.params['p-bc']['update_freq'] == 0:
self.update_pol()
def update_pol(self):
"""
Update the policy via BC on the planner actions.
"""
self.pol.train()
params = self.params['p-bc']
# Generate batches for training
size = min(self.pol_buf.size, self.pol_buf.total_in)
num_inds = params['batch_size'] * params['grad_steps']
inds = np.random.randint(0, size, size=num_inds)
states = self.pol_buf.buffer['s'][inds]
acts = self.pol_buf.buffer['a'][inds]
states = torch.tensor(states, dtype=self.dtype)
actions = torch.tensor(acts, dtype=self.dtype)
for i in range(params['grad_steps']):
bi, ei = i * params['batch_size'], (i + 1) * params['batch_size']
# Train based on L2 distance between actions and predictions
preds = self.pol.forward(states[bi:ei])
preds = torch.squeeze(preds, dim=-1)
targets = torch.squeeze(actions[bi:ei], dim=-1)
loss = torch.nn.functional.mse_loss(preds, targets)
self.pol_optim.zero_grad()
loss.backward()
self.pol_optim.step()
def get_traj_info(self):
"""
Run the policy for a full trajectory and return details about the
trajectory.
"""
env_state = self.env.sim.get_state() if self.mujoco else None
infos = traj.eval_traj(copy.deepcopy(self.env),
env_state,
self.prev_obs,
mujoco=self.mujoco,
perturb=self.perturb,
H=self.H,
gamma=self.gamma,
act_mode='deter',
pt=(self.pol, 0),
terminal=self.val_ens,
tvel=self.tvel)
return infos
def print_logs(self):
"""
BC-specific logging information.
"""
bi, ei = super(BCAgent, self).print_logs()
self.print('BC metrics', mode='head')
self.print('policy traj rew', self.hist['pols'][self.time - 1][0])
self.print('policy traj emp rew', self.hist['pols'][self.time - 1][1])
return bi, ei
def test_policy(self):
"""
Run the BC action selection mechanism.
"""
env = copy.deepcopy(self.env)
obs = env.reset()
if self.tvel is not None:
env.set_target_vel(self.tvel)
obs = env._get_obs()
env_state = env.sim.get_state() if self.mujoco else None
infos = traj.eval_traj(env,
env_state,
obs,
mujoco=self.mujoco,
perturb=self.perturb,
H=self.eval_len,
gamma=1,
act_mode='deter',
pt=(self.pol, 0),
tvel=self.tvel)
self.hist['pol_test'][self.time] = infos[3]
class VPGAgent(Agent):
"""
An agent running online policy gradient. Calling VPGAgent itself uses
REINFORCE, but can be subclassed for other policy gradient class algorithms.
"""
def __init__(self, params):
super(VPGAgent, self).__init__(params)
self.H = self.params['pg']['H']
self.lam = self.params['pg']['lam']
# Initialize policy network
pol_params = self.params['pg']['pol_params']
pol_params['input_size'] = self.N
pol_params['output_size'] = self.M
if 'final_activation' not in pol_params:
pol_params['final_activation'] = torch.tanh
self.pol = MLP(pol_params)
# Std's are not dependent on state
init_log_std = -0.8 * torch.ones(self.M) # ~0.45
self.log_std = torch.nn.Parameter(init_log_std, requires_grad=True)
# Create policy optimizer
ppar = self.params['pg']['pol_optim']
self.pol_params = list(self.pol.parameters()) + [self.log_std]
self.pol_optim = torch.optim.Adam(self.pol_params,
lr=ppar['lr'],
weight_decay=ppar['reg'])
# Create value function and optimizer
val_params = self.params['pg']['val_params']
val_params['input_size'] = self.N
val_params['output_size'] = 1
self.val = MLP(val_params)
vpar = self.params['pg']['val_optim']
self.val_optim = torch.optim.Adam(self.val.parameters(),
lr=vpar['lr'],
weight_decay=vpar['reg'])
# Logging
self.hist['ent'] = np.zeros(self.T)
def get_dist(self, s):
"""
Create a pytorch normal distribution from
the policy network for state s.
"""
s = torch.tensor(s, dtype=self.dtype)
mu = self.pol.forward(s)
std = self.log_std.exp()
return torch.distributions.Normal(mu, std)
def get_ent(self):
"""
Return the current entropy (multivariate Gaussian).
"""
std = self.log_std.exp()
tpe = 2 * np.pi * np.e
return .5 * torch.log(tpe * torch.prod(std))
def get_action(self):
"""
Gets action by running policy.
"""
self.pol.eval()
if self.params['pg']['run_deterministic']:
x = torch.tensor(self.prev_obs, dtype=self.dtype)
act = self.pol.forward(x).detach().cpu().numpy()
else:
act = sample_pol(self.pol, self.log_std, self.prev_obs)
act = np.clip(act, self.params['env']['min_act'],
self.params['env']['max_act'])
self.hist['ent'][self.time] = self.get_ent().detach().cpu().numpy()
return act
def do_updates(self):
"""
Performs actor and critic updates.
"""
if self.time % self.params['pg']['update_every'] == 0 or self.time == 1:
plan_time = 0
H, num_rollouts = self.H, self.params['pg']['num_rollouts']
for i in range(self.params['pg']['num_iter']):
# Sample rollouts using ground truth model
check = time.time()
rollouts = self.sample_rollouts(H, num_rollouts)
plan_time += time.time() - check
# Performs value updates alongside advantage calculation
rews = self.update_pol(rollouts)
# Time spent generating rollouts should be considered planning time
self.hist['plan_time'][self.time - 1] += plan_time
self.hist['update_time'][self.time - 1] -= plan_time
def sample_rollouts(self, H, num_rollouts):
"""
Use traj module to sample rollouts using the policy.
"""
env_state = self.env.sim.get_state() if self.mujoco else None
self.pol.eval()
rollouts = traj.generate_trajectories(
num_rollouts,
self.env,
env_state,
self.prev_obs,
mujoco=self.mujoco,
perturb=self.perturb,
H=self.H,
gamma=self.gamma,
act_mode='gauss',
pt=(sample_pol, self.pol, self.log_std),
terminal=None,
tvel=self.tvel,
num_cpu=self.params['pg']['num_cpu'])
return rollouts
def update_val(self, obs, targets):
"""
Update value function with MSE loss.
"""
preds = self.val.forward(obs)
preds = torch.squeeze(preds, dim=-1)
loss = torch.nn.functional.mse_loss(targets, preds)
self.val_optim.zero_grad()
loss.backward(retain_graph=True)
self.val_optim.step()
return loss.item()
def calc_advs(self, obs, rews, update_vals=True):
"""
Calculate advantages for use of updating the policy (and updating value
function). Can either use rewards-to-go or GAE.
"""
num_rollouts, H = obs.shape[:2]
self.val.eval()
if not self.params['pg']['use_gae']:
# Calculate terminal values
fin_obs = obs[:, -1]
fin_vals = self.val.forward(fin_obs)
fin_vals = torch.squeeze(fin_vals, dim=-1)
# Calculate rewards-to-go
rtg = torch.zeros((num_rollouts, H))
for k in reversed(range(H)):
if k < H - 1:
rtg[:, k] += self.gamma * rtg[:, k + 1]
else:
rtg[:, k] += self.gamma * fin_vals
rtg[:, k] += rews[:, k]
if update_vals:
self.val.train()
self.update_val(obs, rtg)
# Normalize advantages for policy gradient
for k in range(H):
rtg[:, k] -= torch.mean(rtg[:, k])
return rtg
# Generalized Advantage Estimation (GAE)
prev_obs = torch.tensor(self.prev_obs, dtype=self.dtype)
orig_val = self.val.forward(prev_obs)
vals = torch.squeeze(self.val.forward(obs), dim=-1)
deltas = torch.zeros(rews.shape)
advs = torch.zeros((num_rollouts, H))
lg = self.lam * self.gamma
for k in reversed(range(H)):
prev_vals = vals[:, k - 1] if k > 0 else orig_val
deltas[:, k] = self.gamma * vals[:, k] + rews[:, k] - prev_vals
if k == H - 1:
advs[:, k] = deltas[:, k]
else:
advs[:, k] = lg * advs[:, k + 1] + deltas[:, k]
advs = advs.detach()
# Optionally, also update the value functions
if update_vals:
self.val.train()
# It is reasonable to train on advs or deltas
dvals = advs
# Have to perform trick to match deltas with prev vals
fvals = torch.stack([orig_val for _ in range(vals.shape[0])],
dim=0)
rets = torch.cat(
[fvals + dvals[:, :1], vals[:, :-1] + dvals[:, 1:]], dim=-1)
fobs = torch.unsqueeze(prev_obs, dim=0)
fobs = torch.stack([fobs for _ in range(vals.shape[0])], dim=0)
obs = torch.cat([fobs, obs[:, :-1]], dim=1)
self.update_val(obs, rets)
# Normalize advantages for policy gradient
advs -= torch.mean(advs)
advs /= 1e-3 + torch.std(advs)
return advs
def get_pol_loss(self, logprob, advs, orig_logprob=None):
"""
For REINFORCE, the policy loss is thelogprobs times the advatanges. It
is important that the logprobs carry the gradient so that we can
backpropagate through them in the policy update.
"""
return torch.mean(logprob * advs)
def get_logprob(self, pol, log_std, obs, acts):
"""
Get log probabilities for the actions, keeping the gradients.
"""
num_rollouts, H = obs.shape[0:2]
pol.train()
dist = self.get_dist(obs)
logprob = dist.log_prob(acts).sum(-1)
return logprob
def update_pol(self, rollouts, orig_logprob=None):
"""
Update the policy on the on-policy rollouts.
"""
H = rollouts[0][0].shape[0]
self.pol.train()
obs = np.zeros((len(rollouts), self.H, self.N))
acts = np.zeros((len(rollouts), self.H, self.M))
rews = torch.zeros((len(rollouts), self.H))
for i in range(len(rollouts)):
for k in range(self.H):
obs[i, k] = rollouts[i][0][k]
acts[i, k] = rollouts[i][1][k]
rews[i, k] = rollouts[i][2][k]
obs = torch.tensor(obs, dtype=self.dtype)
acts = torch.tensor(acts, dtype=self.dtype)
# Perform updates for multiple steps on the value function
if self.params['pg']['use_gae']:
for _ in range(self.params['pg']['val_steps']):
advs = self.calc_advs(obs, rews, update_vals=True)
else:
advs = self.calc_advs(obs, rews, update_vals=False)
# Perform updates for multiple epochs on the policy
bsize = self.params['pg']['batch_size']
for _ in range(self.params['pg']['pol_steps']):
inds = np.random.permutation(len(rollouts))
binds = inds[:bsize]
bobs, bacts = obs[binds], acts[binds]
brews, badvs = rews[binds], advs[binds]
if orig_logprob is not None:
bprobs = orig_logprob[binds]
else:
bprobs = None
# Get a logprob that has gradients
logprob = self.get_logprob(self.pol, self.log_std, bobs, bacts)
if not self.continue_updates(logprob, bprobs):
break
# Compute policy loss (i.e. gradient ascent)
J = -self.get_pol_loss(logprob, badvs, orig_logprob=bprobs)
# Apply entropy bonus
ent_coef = self.params['pg']['pol_optim']['ent_temp']
if ent_coef != 0:
J -= ent_coef * self.get_ent()
self.pol_optim.zero_grad()
torch.nn.utils.clip_grad_norm_(self.pol.parameters(),
self.params['pg']['grad_clip'])
J.backward()
self.pol_optim.step()
# Clamp stds to be within set bounds
log_min = np.log(self.params['pg']['min_std'])
log_min = torch.tensor(log_min, dtype=self.dtype)
log_max = np.log(self.params['pg']['max_std'])
log_max = torch.tensor(log_max, dtype=self.dtype)
self.log_std.data = torch.clamp(self.log_std.data, log_min,
log_max)
return rews
def continue_updates(self, logprob, orig_logprob=None):
"""
Method for whether or not to continue updates.
"""
return True
def print_logs(self):
"""
Policy gradient-specific logging information.
"""
bi, ei = super(VPGAgent, self).print_logs()
self.print('policy gradient metrics', mode='head')
self.print('entropy avg', np.mean(self.hist['ent'][bi:ei]))
self.print('sigma avg',
np.mean(torch.exp(self.log_std).detach().cpu().numpy()))
return bi, ei
