73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
import torch
|
|
import numpy as np
|
|
import torch.nn.functional as F
|
|
|
|
from tianshou.data import Batch
|
|
from tianshou.policy import BasePolicy
|
|
|
|
|
|
class PGPolicy(BasePolicy):
|
|
"""docstring for PGPolicy"""
|
|
|
|
def __init__(self, model, optim, dist_fn=torch.distributions.Categorical,
|
|
discount_factor=0.99):
|
|
super().__init__()
|
|
self.model = model
|
|
self.optim = optim
|
|
self.dist_fn = dist_fn
|
|
self._eps = np.finfo(np.float32).eps.item()
|
|
assert 0 < discount_factor <= 1, 'discount_factor should in (0, 1]'
|
|
self._gamma = discount_factor
|
|
|
|
def process_fn(self, batch, buffer, indice):
|
|
returns = self._vanilla_returns(batch)
|
|
# returns = self._vectorized_returns(batch)
|
|
batch.update(returns=returns)
|
|
return batch
|
|
|
|
def __call__(self, batch, state=None):
|
|
logits, h = self.model(batch.obs, state=state, info=batch.info)
|
|
logits = F.softmax(logits, dim=1)
|
|
dist = self.dist_fn(logits)
|
|
act = dist.sample()
|
|
return Batch(logits=logits, act=act, state=h, dist=dist)
|
|
|
|
def learn(self, batch, batch_size=None):
|
|
losses = []
|
|
batch.returns = (batch.returns - batch.returns.mean()) \
|
|
/ (batch.returns.std() + self._eps)
|
|
for b in batch.split(batch_size):
|
|
self.optim.zero_grad()
|
|
dist = self(b).dist
|
|
a = torch.tensor(b.act, device=dist.logits.device)
|
|
r = torch.tensor(b.returns, device=dist.logits.device)
|
|
loss = -(dist.log_prob(a) * r).sum()
|
|
loss.backward()
|
|
self.optim.step()
|
|
losses.append(loss.detach().cpu().numpy())
|
|
return {'loss': losses}
|
|
|
|
def _vanilla_returns(self, batch):
|
|
returns = batch.rew[:]
|
|
last = 0
|
|
for i in range(len(returns) - 1, -1, -1):
|
|
if not batch.done[i]:
|
|
returns[i] += self._gamma * last
|
|
last = returns[i]
|
|
return returns
|
|
|
|
def _vectorized_returns(self, batch):
|
|
# according to my tests, it is slower than vanilla
|
|
# import scipy.signal
|
|
convolve = np.convolve
|
|
# convolve = scipy.signal.convolve
|
|
rew = batch.rew[::-1]
|
|
batch_size = len(rew)
|
|
gammas = self._gamma ** np.arange(batch_size)
|
|
c = convolve(rew, gammas)[:batch_size]
|
|
T = np.where(batch.done[::-1])[0]
|
|
d = np.zeros_like(rew)
|
|
d[T] += c[T] - rew[T]
|
|
d[T[1:]] -= d[T[:-1]] * self._gamma ** np.diff(T)
|
|
return (c - convolve(d, gammas)[:batch_size])[::-1]
|