def evaluate(self, state, epsilon=1e-6):
'''
generate sampled action with state as input wrt the policy network;
'''
mean, log_std = self.forward(state)
std = log_std.exp() # no clip in evaluation, clip affects gradients flow
normal = Normal(0, 1)
z = normal.sample()
action_0 = torch.tanh(mean + std*z.to(device)) # TanhNormal distribution as actions; reparameterization trick
action = self.action_range*action_0
# The log-likelihood here is for the TanhNorm distribution instead of only Gaussian distribution. \
# The TanhNorm forces the Gaussian with infinite action range to be finite. \
# For the three terms in this log-likelihood estimation: \
# (1). the first term is the log probability of action as in common \
# stochastic Gaussian action policy (without Tanh); \
# (2). the second term is the caused by the Tanh(), \
# as shown in appendix C. Enforcing Action Bounds of https://arxiv.org/pdf/1801.01290.pdf, \
# the epsilon is for preventing the negative cases in log; \
# (3). the third term is caused by the action range I used in this code is not (-1, 1) but with \
# an arbitrary action range, which is slightly different from original paper.
log_prob = Normal(mean, std).log_prob(mean+ std*z.to(device)) - torch.log(1. - action_0.pow(2) + epsilon) - np.log(self.action_range)
# both dims of normal.log_prob and -log(1-a**2) are (N,dim_of_action);
# the Normal.log_prob outputs the same dim of input features instead of 1 dim probability,
# needs sum up across the features dim to get 1 dim prob; or else use Multivariate Normal.
log_prob = log_prob.sum(dim=1, keepdim=True)
return action, log_prob, z, mean, log_std
def evaluate(self, state, epsilon=1e-6):
'''
generate sampled action with state as input wrt the policy network;
deterministic evaluation provides better performance according to the original paper;
'''
mean, log_std = self.forward(state)
std = log_std.exp(
) # no clip in evaluation, clip affects gradients flow
normal = Normal(0, 1)
z = normal.sample()
action_0 = torch.tanh(
mean + std * z.to(device)
) # TanhNormal distribution as actions; reparameterization trick
action = self.action_range * action_0
''' stochastic evaluation '''
log_prob = Normal(
mean, std).log_prob(mean + std * z.to(device)) - torch.log(
1. - action_0.pow(2) + epsilon) - np.log(self.action_range)
''' deterministic evaluation '''
# log_prob = Normal(mean, std).log_prob(mean) - torch.log(1. - torch.tanh(mean).pow(2) + epsilon) - np.log(self.action_range)
'''
both dims of normal.log_prob and -log(1-a**2) are (N,dim_of_action);
the Normal.log_prob outputs the same dim of input features instead of 1 dim probability,
needs sum up across the features dim to get 1 dim prob; or else use Multivariate Normal.
'''
log_prob = log_prob.sum(dim=-1, keepdim=True)
return action, log_prob, z, mean, log_std
def evaluate_action(self, state):
'''
evaluate action within GPU graph, for gradients flowing through it
'''
state = torch.FloatTensor(state).unsqueeze(0).to(device) # state dim: (N, dim of state)
if DETERMINISTIC:
action = self.forward(state)
return action.detach().cpu().numpy()
elif DISCRETE and not DETERMINISTIC: # actor-critic (discrete)
probs = self.forward(state)
m = Categorical(probs)
action = m.sample().to(device)
log_prob = m.log_prob(action)
return action.detach().cpu().numpy(), log_prob.squeeze(0), m.entropy().mean()
elif not DISCRETE and not DETERMINISTIC: # soft actor-critic (continuous)
self.action_range = 30.
self.epsilon = 1e-6
mean, log_std = self.forward(state)
std = log_std.exp()
normal = Normal(0, 1)
z = normal.sample().to(device)
action0 = torch.tanh(mean + std*z.to(device)) # TanhNormal distribution as actions; reparameterization trick
action = self.action_range * action0
log_prob = Normal(mean, std).log_prob(mean+ std*z.to(device)) - torch.log(1. - action0.pow(2) + self.epsilon) - np.log(self.action_range)
log_prob = log_prob.sum(dim=1, keepdim=True)
print('mean: ', mean, 'log_std: ', log_std)
# return action.item(), log_prob, z, mean, log_std
return action.detach().cpu().numpy().squeeze(0), log_prob.squeeze(0), Normal(mean, std).entropy().mean()
def sample_actions_and_llhoods_for_all_skills(self, s, explore=True):
x = s.clone().view(s.size(0), 1,
s.size(1)).repeat(1, self.n_m_actions, 1)
m, log_stdev = self(x)
stdev = log_stdev.exp()
if explore:
u = m + stdev * torch.randn_like(m)
else:
u = m
a = torch.tanh(u)
if self.log_func == 'self':
llhoods = gaussian_likelihood(u.unsqueeze(1), m.unsqueeze(2),
log_stdev.unsqueeze(2),
self.EPS_sigma)
elif self.log_func == 'torch':
llhoods = Normal(m.unsqueeze(2),
stdev.unsqueeze(2)).log_prob(u.unsqueeze(1))
if self.log_lim_method == 'clamp':
llhoods -= torch.log(
torch.clamp(1 - a.unsqueeze(1).pow(2), self.EPS_log_1_min_a2,
1.0))
elif self.log_lim_method == 'sum':
llhoods -= torch.log(1 - a.unsqueeze(1).pow(2) +
self.EPS_log_1_min_a2)
llhoods = llhoods.sum(
3) #.clamp(self.min_log_stdev, self.max_log_stdev)
return a, llhoods
def step(self, image, location, recurrent_hidden):
# image = [batch size, n channels, height, width]
# location = [batch size, 2]
# recurrent_hidden = [batch size, recurrent hid dim]
glimpse_hidden = self.glimpse_network(image, location)
# glimpse_hidden = [batch size, glimpse hid dim + location hid dim]
recurrent_hidden = self.core_network(glimpse_hidden, recurrent_hidden)
# recurrent_hidden = [batch size, recurrent hid dim]
location, location_mu = self.location_network(recurrent_hidden)
# location = [batch size, 2]
# location_mu = [batch size, 2]
log_location_action = Normal(location_mu, self.std).log_prob(location)
log_location_action = log_location_action.sum(dim=1)
# log_location_action = [batch size]
baseline = self.baseline_network(recurrent_hidden)
return recurrent_hidden, log_location_action, baseline, location, location_mu,
def evaluate(self,
state,
smooth_policy,
device=torch.device("cpu"),
epsilon=1e-6):
mean, log_std = self.forward(state)
normal = Normal(torch.zeros(mean.shape), torch.ones(log_std.shape))
z = normal.sample().to(device)
std = log_std.exp()
if self.args.stochastic_actor:
z = torch.clamp(z, -3, 3)
action_0 = mean + torch.mul(z, std)
action_1 = torch.tanh(action_0)
action = torch.mul(self.action_range.to(device),
action_1) + self.action_bias.to(device)
log_prob = Normal(mean, std).log_prob(action_0) - torch.log(
1. - action_1.pow(2) + epsilon) - torch.log(
self.action_range.to(device))
log_prob = log_prob.sum(dim=-1, keepdim=True)
return action, log_prob, std.detach()
else:
action_mean = torch.mul(
self.action_range.to(device),
torch.tanh(mean)) + self.action_bias.to(device)
smooth_random = torch.clamp(0.2 * z, -0.5, 0.5)
action_random = action_mean + smooth_random
action_random = torch.min(action_random,
self.action_high.to(device))
action_random = torch.max(action_random,
self.action_low.to(device))
action = action_random if smooth_policy else action_mean
return action, 0 * log_std.sum(dim=-1, keepdim=True), std.detach()
def get_action(self, state, deterministic, epsilon=1e-6):
mean, log_std = self.forward(state)
normal = Normal(torch.zeros(mean.shape), torch.ones(log_std.shape))
z = normal.sample()
if self.args.stochastic_actor:
std = log_std.exp()
action_0 = mean + torch.mul(z, std)
action_1 = torch.tanh(action_0)
action = torch.mul(self.action_range, action_1) + self.action_bias
log_prob = Normal(mean, std).log_prob(action_0) - torch.log(
1. - action_1.pow(2) + epsilon) - torch.log(self.action_range)
log_prob = log_prob.sum(dim=-1, keepdim=True)
action_mean = torch.mul(self.action_range,
torch.tanh(mean)) + self.action_bias
action = action_mean.detach().cpu().numpy(
) if deterministic else action.detach().cpu().numpy()
return action, log_prob.detach().item()
else:
action_mean = torch.mul(self.action_range,
torch.tanh(mean)) + self.action_bias
action = action_mean + 0.1 * torch.mul(self.action_range, z)
action = torch.min(action, self.action_high)
action = torch.max(action, self.action_low)
action = action_mean.detach().cpu().numpy(
) if deterministic else action.detach().cpu().numpy()
return action, 0
def evaluate(self, state, deterministic, eval_noise_scale, epsilon=1e-6):
'''
generate action with state as input wrt the policy network, for calculating gradients
'''
mean, log_std = self.forward(state)
std = log_std.exp(
) # no clip in evaluation, clip affects gradients flow
normal = Normal(0, 1)
z = normal.sample()
action_0 = torch.tanh(
mean + std * z.to(device)
) # TanhNormal distribution as actions; reparameterization trick
action = self.action_range * mean if deterministic else self.action_range * action_0
log_prob = Normal(
mean, std).log_prob(mean + std * z.to(device)) - torch.log(
1. - action_0.pow(2) + epsilon) - np.log(self.action_range)
# both dims of normal.log_prob and -log(1-a**2) are (N,dim_of_action);
# the Normal.log_prob outputs the same dim of input features instead of 1 dim probability,
# needs sum up across the features dim to get 1 dim prob; or else use Multivariate Normal.
log_prob = log_prob.sum(dim=1, keepdim=True)
''' add noise '''
eval_noise_clip = 2 * eval_noise_scale
noise = normal.sample(action.shape) * eval_noise_scale
noise = torch.clamp(noise, -eval_noise_clip, eval_noise_clip)
action = action + noise.to(device)
return action, log_prob, z, mean, log_std
def get_KL(self, params, old_log_prob, state, old_action_raw, old_action):
torch.nn.utils.vector_to_parameters(params, self.actor.evaluate_net.parameters())
mean, log_std = self.actor.evaluate_net.forward(state)
std = log_std.exp()
new_log_prob = Normal(mean, std).log_prob(old_action_raw) - torch.log(1 - old_action.pow(2) + 1e-6)
new_log_prob = new_log_prob.sum(-1, keepdim=True)
KL = old_log_prob - new_log_prob
KL = KL.mean()
return KL.item()
def evaluate(self, state, epsilon=1e-6):
mean, log_std = self.forward(state)
std = log_std.exp()
normal = Normal(0, 1)
z = normal.sample()
action = torch.tanh(mean+ std*z.to(device))
log_prob = Normal(mean, std).log_prob(mean+ std*z.to(device)) - torch.log(1 - action.pow(2) + epsilon)
log_prob = log_prob.sum(dim=-1, keepdim=True)
return action, log_prob, z, mean, log_std
def get_log_probs(self, obs_rest, epsilon=1e-6):
mean, log_std = self.forward(obs_rest)
std = log_std.exp(
) # no clip in evaluation, clip affects gradients flow
action_logit = Normal(mean, std).sample()
action = torch.tanh(action_logit)
log_prob = Normal(
mean, std).log_prob(action_logit) - torch.log(1. - action.pow(2) +
epsilon)
#assert float(log_prob.mean())==float(log_prob.mean()), "Log_prob is nan"
return log_prob.sum(dim=1, keepdim=True), action
def sample_actions_and_llhoods(self, s, explore=True):
m, std = self(s)
if explore:
u = m + std * torch.randn_like(m)
else:
u = m
a = torch.tanh(u)
llhoods = Normal(m, std.abs()).log_prob(u)
llhoods -= torch.log(1 - a.pow(2) + 1e-6)
llhoods = llhoods.sum(1, keepdim=True)
return a, llhoods
def get_reward(self, state):
same_z = Normal(0, 1).sample()
mean, log_std = self.actor.policy_net.forward(state)
std = log_std.exp()
action_raw = mean + std * same_z
action = torch.tanh(action_raw)
log_prob = Normal(mean, std).log_prob(action_raw) - torch.log(1 - action.pow(2) + 1e-6)
log_prob = log_prob.sum(-1, keepdim=True)
predicted_new_q_value_1, predicted_new_q_value_2 = self.critic.predict_q(state, action)
predicted_new_q_value = torch.min(predicted_new_q_value_1, predicted_new_q_value_2)
loss = (predicted_new_q_value - self.alpha * log_prob).mean()
return loss.item()
def get_action(self, x):
mean, log_std = self.pi(x)
std = log_std.exp()
normal = Normal(0, 1)
z = normal.sample()
action = mean + std*z
log_prob = Normal(mean, std).log_prob(action)
log_prob = log_prob.sum(dim=-1, keepdim=True) # reduce dim
prob = log_prob.exp()
action = self.action_range*action # scale the action
return action.detach().numpy(), prob
def evaluate(self, state, epsilon=1e-6):
mean, log_std = self.forward(state)
std = log_std.exp()
normal = Normal(torch.zeros(mean.shape), torch.ones(std.shape))
z = normal.sample().to(device)
action_0 = mean + torch.mul(z, std)
action_1 = torch.tanh(action_0)
action = torch.mul(self.action_range.to(device), action_1) + self.action_bias.to(device)
log_prob = Normal(mean, std).log_prob(action_0)-torch.log(1. - action_1.pow(2) + epsilon) - torch.log(self.action_range.to(device))
log_prob = log_prob.sum(dim=-1, keepdim=True)
entropy = Normal(mean, std).entropy()
return action, log_prob, entropy, mean.detach(), std.detach()
def evaluate(self,
data,
numerical_state,
epsilon=1e-6): #must check how to fine-tune it.
mean, log_std = self.forward(data, numerical_state)
std = torch.exp(log_std)
policy = (
mean + std *
Normal(torch.zeros(4), torch.ones(4)).sample().to(self.device))
policy.requires_grad_()
action = torch.tanh(policy)
log_prob = Normal(torch.zeros(4).to(self.device), torch.ones(4).to(self.device)).\
log_prob(policy) - torch.log(1 - action.pow(2) + epsilon)
log_prob = log_prob.sum(dim=1, keepdim=True)
return action, log_prob, policy, mean, log_std
def learn(self):
state, action, reward, next_state, end = self.memory.sample(self.batch_size)
state = torch.FloatTensor(state).to(device)
action = torch.FloatTensor(action).to(device)
reward = torch.FloatTensor(reward).unsqueeze(1).to(device)
next_state = torch.FloatTensor(next_state).to(device)
end = torch.FloatTensor(np.float32(end)).unsqueeze(1).to(device)
# Training Q Networks
predicted_q_value_1, predicted_q_value_2 = self.critic.predict_q(state, action)
predicted_v_target = self.critic.predict_v_target(next_state)
target_q_value = reward + (1 - end) * self.discount * predicted_v_target
q_loss_1 = nn.MSELoss()(predicted_q_value_1, target_q_value.detach())
q_loss_2 = nn.MSELoss()(predicted_q_value_2, target_q_value.detach())
self.critic.learn_q(q_loss_1, q_loss_2)
# Training V Network
new_action, log_prob = self.actor.predict(state)
predicted_new_q_value_1, predicted_new_q_value_2 = self.critic.predict_q(state, new_action)
predicted_new_q_value = torch.min(predicted_new_q_value_1, predicted_new_q_value_2)
target_v_value = predicted_new_q_value - self.alpha * log_prob
predicted_v_value = self.critic.predict_v(state)
v_loss = nn.MSELoss()(predicted_v_value, target_v_value.detach())
self.critic.learn_v(v_loss)
if self.debug_file is not None:
z = Normal(0, 1).sample().to(device)
mean, log_std = self.actor.policy_net.forward(state)
std = log_std.exp()
old_action_raw = mean + std * z
old_action = torch.tanh(old_action_raw)
old_log_prob = Normal(mean, std).log_prob(old_action_raw) - torch.log(1 - old_action.pow(2) + 1e-6)
old_log_prob = old_log_prob.sum(-1, keepdim=True)
old_reward = self.get_reward(state)
# Training Policy Network
policy_loss = (self.alpha * log_prob - predicted_new_q_value).mean()
self.actor.learn(policy_loss)
if self.debug_file is not None:
KL = self.get_KL(torch.nn.utils.parameters_to_vector(self.actor.policy_net.parameters()),
old_log_prob, state, old_action_raw, old_action)
new_reward = self.get_reward(state)
self.debug_file.write("{},{}\n".format(abs(KL), new_reward - old_reward))
# Updating Target-V Network
self.critic.update_target_v()
def evaluate(self, state, epsilon=1e-6):
'''
generate sampled action with state as input wrt the policy network;
'''
mean, log_std, d_action_prob = self.forward(state)
std = log_std.exp()
normal = Normal(0, 1)
z = normal.sample().to(d)
c_action_0 = torch.tanh(mean + std * z)
c_action = self.action_range * c_action_0
log_prob = Normal(mean, std).log_prob(mean + std * z) - torch.log(
1. - c_action_0.pow(2) + epsilon) - np.log(self.action_range)
log_prob = log_prob.sum(dim=-1, keepdim=True)
return d_action_prob, c_action, log_prob, z, mean, log_std
def forward(self, y, X):
# Sample parameters
b = self.b.rsample()
sig = self.sig.rsample()
# Compute loglike
ll = Normal(X.matmul(b), sig).log_prob(y)
# Compute kl_qp
kl_qp = kld(self.b.dist(), Normal(0, 1)) + kld(self.sig.dist(),
Gamma(1, 1))
# Compute ELBO
elbo = ll.sum() - kl_qp.sum()
return elbo
def elbo(self,
qz_m,
qz_logv,
zode_L,
logpL,
X,
XrecL,
L,
qz_enc_m=None,
qz_enc_logv=None):
''' Input:
qz_m - latent means [N,2q]
qz_logv - latent logvars [N,2q]
zode_L - latent trajectory samples [L,N,T,2q]
logpL - densities of latent trajectory samples [L,N,T]
X - input images [N,T,nc,d,d]
XrecL - reconstructions [L,N,T,nc,d,d]
qz_enc_m - encoder density means [N*T,2*q]
qz_enc_logv - encoder density variances [N*T,2*q]
'''
[N, T, nc, d, d] = X.shape
q = qz_m.shape[1] // 2
# prior
log_pzt = self.mvn.log_prob(zode_L.contiguous().view(
[L * N * T, 2 * q])) # L*N*T
log_pzt = log_pzt.view([L, N, T]) # L,N,T
kl_zt = logpL - log_pzt # L,N,T
kl_z = kl_zt.sum(2).mean(0) # N
# likelihood
XL = X.repeat([L, 1, 1, 1, 1, 1]) # L,N,T,nc,d,d
lhood_L = torch.log(XrecL) * XL + torch.log(1 - XrecL) * (
1 - XL) # L,N,T,nc,d,d
lhood = lhood_L.sum([2, 3, 4, 5]).mean(0) # N
if qz_enc_m is not None: # instant encoding
qz_enc_mL = qz_enc_m.repeat([L, 1]) # L*N*T,2*q
qz_enc_logvL = qz_enc_logv.repeat([L, 1]) # L*N*T,2*q
mean_ = qz_enc_mL.contiguous().view(-1) # L*N*T*2*q
std_ = qz_enc_logvL.exp().contiguous().view(-1) # L*N*T*2*q
qenc_zt_ode = Normal(mean_, std_).log_prob(
zode_L.contiguous().view(-1)).view([L, N, T, 2 * q])
qenc_zt_ode = qenc_zt_ode.sum([3]) # L,N,T
inst_enc_KL = logpL - qenc_zt_ode
inst_enc_KL = inst_enc_KL.sum(2).mean(0) # N
return lhood.mean(), kl_z.mean(), inst_enc_KL.mean()
else:
return lhood.mean(), kl_z.mean() # mean over training samples
def evaluate(self, state, last_action, hidden_in, epsilon=1e-6):
'''
generate sampled action with state as input wrt the policy network;
'''
mean, log_std, hidden_out = self.forward(state, last_action, hidden_in)
std = log_std.exp() # no clip in evaluation, clip affects gradients flow
normal = Normal(0, 1)
z = normal.sample()
action_0 = torch.tanh(mean + std * z.cuda()) # TanhNormal distribution as actions; reparameterization trick
action = self.action_range * action_0
log_prob = Normal(mean, std).log_prob(mean + std * z.cuda()) - torch.log(
1. - action_0.pow(2) + epsilon) - np.log(self.action_range)
# both dims of normal.log_prob and -log(1-a**2) are (N,dim_of_action);
# the Normal.log_prob outputs the same dim of input features instead of 1 dim probability,
# needs sum up across the features dim to get 1 dim prob; or else use Multivariate Normal.
log_prob = log_prob.sum(dim=-1, keepdim=True)
return action, log_prob, z, mean, log_std, hidden_out
def sample_action(self, s):
mean, log_std = self.forward(s)
std = log_std.exp()
# calculate action using reparameterization trick and action scaling
normal = Normal(0, 1)
xi = normal.sample()
u = mean + std * xi.to(hyp.device)
y = torch.tanh(u)
a = y * self.action_scale + self.action_bias
# enforcing action bound (appendix of paper)
log_pi = Normal(
mean, std).log_prob(u) - torch.log(self.action_scale *
(1 - y.pow(2)) + hyp.EPSILON)
log_pi = log_pi.sum(1, keepdim=True)
mean = torch.tanh(mean) * self.action_scale + self.action_bias
return a, log_pi, mean
def elbo(self, qz_m, qz_logv, zode_L, logpL, X, XrecL, Ndata, qz_enc_m=None, qz_enc_logv=None):
''' Input:
qz_m - latent means [N,2q]
qz_logv - latent logvars [N,2q]
zode_L - latent trajectory samples [L,N,T,2q]
logpL - densities of latent trajectory samples [L,N,T]
X - input images [N,T,nc,d,d]
XrecL - reconstructions [L,N,T,nc,d,d]
Ndata - number of sequences in the dataset (required for elbo
qz_enc_m - encoder density means [N*T,2*q]
qz_enc_logv - encoder density variances [N*T,2*q]
Returns:
likelihood
prior on ODE trajectories KL[q_ode(z_{0:T})||N(0,I)]
prior on BNN weights
instant encoding term KL[q_ode(z_{0:T})||q_enc(z_{0:T}|X_{0:T})] (if required)
'''
[N,T,nc,d,d] = X.shape
L = zode_L.shape[0]
q = qz_m.shape[1]//2
# prior
log_pzt = self.mvn.log_prob(zode_L.contiguous().view([L*N*T,2*q])) # L*N*T
log_pzt = log_pzt.view([L,N,T]) # L,N,T
kl_zt = logpL - log_pzt # L,N,T
kl_z = kl_zt.sum(2).mean(0) # N
kl_w = self.bnn.kl().sum()
# likelihood
XL = X.repeat([L,1,1,1,1,1]) # L,N,T,nc,d,d
lhood_L = torch.log(1e-3+XrecL)*XL + torch.log(1e-3+1-XrecL)*(1-XL) # L,N,T,nc,d,d
lhood = lhood_L.sum([2,3,4,5]).mean(0) # N
if qz_enc_m is not None: # instant encoding
qz_enc_mL = qz_enc_m.repeat([L,1]) # L*N*T,2*q
qz_enc_logvL = qz_enc_logv.repeat([L,1]) # L*N*T,2*q
mean_ = qz_enc_mL.contiguous().view(-1) # L*N*T*2*q
std_ = 1e-3+qz_enc_logvL.exp().contiguous().view(-1) # L*N*T*2*q
qenc_zt_ode = Normal(mean_,std_).log_prob(zode_L.contiguous().view(-1)).view([L,N,T,2*q])
qenc_zt_ode = qenc_zt_ode.sum([3]) # L,N,T
inst_enc_KL = logpL - qenc_zt_ode
inst_enc_KL = inst_enc_KL.sum(2).mean(0) # N
return Ndata*lhood.mean(), Ndata*kl_z.mean(), kl_w, Ndata*inst_enc_KL.mean()
else:
return Ndata*lhood.mean(), Ndata*kl_z.mean(), kl_w
def step(self, x, l_t, h_t):
"""
@param x: image. (batch, channel, height, width)
@param l_t: location trial. (batch, 2)
@param h_t: last hidden state. (batch, rnn_hidden)
@return h_t: next hidden state. (batch, rnn_hidden)
@return l_t: next location trial. (batch, 2)
@return b_t: baseline for step t. (batch)
@return log_pi: probability for next location trial. (batch)
"""
glimpse = self.glimpse_net(x, l_t)
h_t = self.rnn(glimpse, h_t)
mu, l_t = self.location_net(h_t)
b_t = self.baseline_net(h_t).squeeze()
log_pi = Normal(mu, self.std).log_prob(l_t)
# Note: log(p_y*p_x) = log(p_y) + log(p_x)
log_pi = log_pi.sum(dim=1)
return h_t, l_t, b_t, log_pi
def so3_entropy_old(w_eps, std, k=10):
'''
w_eps(Tensor of dim 3): sample from so3
covar(Tensor of dim 3x3): covariance of distribution on so3
k: 2k+1 samples for truncated summation
'''
# entropy of gaussian distribution on so3
# see appendix C of https://arxiv.org/pdf/1807.04689.pdf
theta = w_eps.norm(p=2)
u = w_eps / theta # 3
angles = 2 * np.pi * torch.arange(
-k, k + 1, dtype=w_eps.dtype, device=w_eps.device) # 2k+1
theta_hat = theta + angles # 2k+1
x = u[None, :] * theta_hat[:, None] # 2k+1 , 3
log_p = Normal(torch.zeros(3, device=w_eps.device),
std).log_prob(x) # 2k+1,3
clamp = 1e-3
log_vol = torch.log((theta_hat**2).clamp(min=clamp) /
(2 - 2 * torch.cos(theta)).clamp(min=clamp)) # 2k+1
log_p = log_p.sum(-1) + log_vol
entropy = -logsumexp(log_p)
return entropy
def sample(mod, lam_draw, y_grid=None):
if y_grid is None:
upper = 6
lower = -6
grid_size = 100
step = (upper - lower) / grid_size
y_grid = torch.arange(start=-6, end=6, step=step)
# TODO: TEST
gam, mu, sig = gam_post.sample(mod, lam_draw)
dden = []
for i in range(mod.I):
gami_onehot = util.get_one_hot(gam[i], sum(mod.L))
obs_i = 1 - mod.m[i]
mu_i = (gami_onehot * mu[None, None, :]).sum(-1)
dden_i = Normal(mu_i[:, :, None],
sig[i]).log_prob(y_grid[None, None, :]).exp()
dden_i = dden_i * obs_i[:, :, None].double()
dden_i = dden_i.sum(0) / obs_i.sum(0, keepdim=True).double().transpose(
0, 1)
dden.append(dden_i)
return (y_grid, dden)
def so3_entropy(w_eps, std, k=10):
'''
w_eps(Tensor of dim Bx3): sample from so3
std(Tensor of dim Bx3): std of distribution on so3
k: Use 2k+1 samples for truncated summation
'''
# entropy of gaussian distribution on so3
# see appendix C of https://arxiv.org/pdf/1807.04689.pdf
theta = w_eps.norm(p=2, dim=-1, keepdim=True) # [B, 1]
u = w_eps / theta # [B, 3]
angles = 2 * np.pi * torch.arange(
-k, k + 1, dtype=w_eps.dtype, device=w_eps.device) # 2k+1
theta_hat = theta[:, None, :] + angles[:, None] # [B, 2k+1, 1]
x = u[:, None, :] * theta_hat # [B, 2k+1 , 3]
log_p = Normal(torch.zeros(3, device=w_eps.device),
std).log_prob(x.permute([1, 0, 2])) # [2k+1, B, 3]
log_p = log_p.permute([1, 0, 2]) # [B, 2k+1, 3]
clamp = 1e-3
log_vol = torch.log(
(theta_hat**2).clamp(min=clamp) /
(2 - 2 * torch.cos(theta_hat)).clamp(min=clamp)) # [B, 2k+1, 1]
log_p = log_p.sum(-1) + log_vol.sum(-1) #[B, 2k+1]
entropy = -logsumexp(log_p, -1)
return entropy
def forward(self, state, deterministic=False):
x = F.relu(self.linear1(state))
x = F.relu(self.linear2(x))
mean = self.mean_linear(x)
log_std = self.log_std_linear(x)
log_std = torch.clamp(log_std, self.log_std_min, self.log_std_max)
std = torch.exp(log_std)
log_prob = None
if deterministic:
action = torch.tanh(mean)
else:
normal = Normal(0, 1)
z = mean + std * normal.sample().to(
torch.device("cuda" if torch.cuda.is_available() else "cpu"))
action = torch.tanh(z)
log_prob = Normal(
mean,
std).log_prob(z) - torch.log(1 - action.pow(2) + self.epsilon)
log_prob = log_prob.sum(dim=1, keepdim=True)
return action, mean, log_std, log_prob, std
def learn(self):
state, action, reward, next_state, end = self.memory.sample(
self.batch_size)
state = torch.FloatTensor(state).to(device)
action = torch.FloatTensor(action).to(device)
reward = torch.FloatTensor(reward).unsqueeze(1).to(device)
next_state = torch.FloatTensor(next_state).to(device)
end = torch.FloatTensor(np.float32(end)).unsqueeze(1).to(device)
# Training Q Networks
predicted_q_value_1, predicted_q_value_2 = self.critic.predict_q(
state, action)
predicted_v_target = self.critic.predict_v_target(next_state)
target_q_value = reward + (1 -
end) * self.discount * predicted_v_target
q_loss_1 = nn.MSELoss()(predicted_q_value_1, target_q_value.detach())
q_loss_2 = nn.MSELoss()(predicted_q_value_2, target_q_value.detach())
self.critic.learn_q(q_loss_1, q_loss_2)
# Training V Network
new_action, log_prob = self.actor.predict(state)
predicted_new_q_value_1, predicted_new_q_value_2 = self.critic.predict_q(
state, new_action)
predicted_new_q_value = torch.min(predicted_new_q_value_1,
predicted_new_q_value_2)
target_v_value = predicted_new_q_value - self.alpha * log_prob
predicted_v_value = self.critic.predict_v(state)
v_loss = nn.MSELoss()(predicted_v_value, target_v_value.detach())
self.critic.learn_v(v_loss)
# Training Policy Network
policy_loss = (self.alpha * log_prob - predicted_new_q_value).mean()
normal = Normal(0, 1)
z = normal.sample().to(device)
mean, log_std = self.actor.policy_net.forward(state)
std = log_std.exp()
old_action_raw = mean + std * z
old_action = torch.tanh(old_action_raw)
old_log_prob = Normal(mean, std).log_prob(old_action_raw) - torch.log(
1 - old_action.pow(2) + 1e-6)
old_log_prob = old_log_prob.sum(-1, keepdim=True)
if self.debug_file is not None:
old_reward = self.get_reward(state)
params = torch.nn.utils.parameters_to_vector(
self.actor.policy_net.parameters())
search_direction = torch.nn.utils.parameters_to_vector(
torch.autograd.grad(policy_loss,
self.actor.policy_net.parameters(),
retain_graph=True))
unit_size = torch.FloatTensor([1e-4]).to(device)
max_iteration = 5
# Now we have the iterations
for i in range(max_iteration):
test_params = params - search_direction * unit_size
KL = self.get_KL(test_params, old_log_prob, state, old_action_raw,
old_action)
if abs(KL) <= self.tr:
params = test_params
torch.nn.utils.vector_to_parameters(
params, self.actor.policy_net.parameters())
# Compute new direction
new_action, log_prob = self.actor.predict(state)
predicted_new_q_value_1, predicted_new_q_value_2 = self.critic.predict_q(
state, new_action)
predicted_new_q_value = torch.min(predicted_new_q_value_1,
predicted_new_q_value_2)
policy_loss = (self.alpha * log_prob -
predicted_new_q_value).mean()
search_direction = torch.nn.utils.parameters_to_vector(
torch.autograd.grad(policy_loss,
self.actor.policy_net.parameters(),
retain_graph=True))
else:
break
if self.debug_file is not None:
KL = self.get_KL(
torch.nn.utils.parameters_to_vector(
self.actor.policy_net.parameters()), old_log_prob, state,
old_action_raw, old_action)
new_reward = self.get_reward(state)
self.debug_file.write("{},{}\n".format(abs(KL),
new_reward - old_reward))
# Updating Target-V Network
self.critic.update_target_v()
total_ll = 0.0
aleatoric = 0.0
epistemic = 0.0
# total_d_ll = 0.0
for i, (x, y) in enumerate(test):
x, y = x.to(device), y.to(device)
mus = torch.zeros(args.samples, x.size(0), device=device)
logvars = torch.zeros(args.samples,
x.size(0),
device=device)
for j in range(args.samples):
mus[j], logvars[j], _ = model(x)
ll = Normal(mus, torch.exp(logvars / 2)).log_prob(y)
total_ll += torch.logsumexp(ll.sum(dim=1), dim=0).item()
# mean = mus.mean(dim=0)
# std = (
# mus.var(dim=0) + torch.exp(logvars / 2).mean(dim=0) ** 2
# ) ** 0.5
# total_d_ll += Normal(mean, std).log_prob(y).sum()
epistemic += mus.var(dim=0).mean().item()
aleatoric += torch.exp(logvars).mean(dim=0).sum().item()
real_y = y * test.dataset.y_sigma + test.dataset.y_mu # type: ignore
real_mu = mus.mean(
dim=0
) * test.dataset.y_sigma + test.dataset.y_mu # type: ignore
squared_err += ((real_y - real_mu)**2).sum().item()