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import numpy as np
import torch
import math
import torch.nn.functional as F
import utils
from torch import nn
from torch import distributions as pyd
class TanhTransform(pyd.transforms.Transform):
domain = pyd.constraints.real
codomain = pyd.constraints.interval(-1.0, 1.0)
bijective = True
sign = +1
def __init__(self, cache_size=1):
super().__init__(cache_size=cache_size)
@staticmethod
def atanh(x):
return 0.5 * (x.log1p() - (-x).log1p())
def __eq__(self, other):
return isinstance(other, TanhTransform)
def _call(self, x):
return x.tanh()
def _inverse(self, y):
return self.atanh(y)
def log_abs_det_jacobian(self, x, y):
return 2. * (math.log(2.) - x - F.softplus(-2. * x))
class SquashedNormal(pyd.transformed_distribution.TransformedDistribution):
def __init__(self, loc, scale):
self.loc = loc
self.scale = scale
self.base_dist = pyd.Normal(loc, scale)
transforms = [TanhTransform()]
super().__init__(self.base_dist, transforms)
@property
def mean(self):
mu = self.loc
for tr in self.transforms:
mu = tr(mu)
return mu
class DiagGaussianActor(nn.Module):
"""torch.distributions implementation of an diagonal Gaussian policy."""
def __init__(self, obs_dim, action_dim, hidden_dim, hidden_depth,
log_std_bounds):
super().__init__()
self.log_std_bounds = log_std_bounds
self.trunk = utils.mlp(obs_dim, hidden_dim, 2 * action_dim, hidden_depth)
self.outputs = dict()
self.apply(utils.weight_init)
def forward(self, obs):
mu, log_std = self.trunk(obs).chunk(2, dim=-1)
# constrain log_std inside [log_std_min, log_std_max]
log_std = torch.tanh(log_std)
log_std_min, log_std_max = self.log_std_bounds
log_std = log_std_min + 0.5 * (log_std_max - log_std_min) * (log_std + 1)
std = log_std.exp()
self.outputs['mu'] = mu
self.outputs['std'] = std
dist = SquashedNormal(mu, std)
return dist
def log(self, logger, step):
for k, v in self.outputs.items():
logger.log_histogram(f'train_actor/{k}_hist', v, step)
for i, m in enumerate(self.trunk):
if type(m) == nn.Linear:
logger.log_param(f'train_actor/fc{i}', m, step) |