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PG_Transformer(bad,aggregate-by-sum).py
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PG_Transformer(bad,aggregate-by-sum).py
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"""
This is the Transformer version, where the state vector is 9 propositions
= 9 x 3 = 27-vector
============================================================
Policy Gradient, Reinforcement Learning. Adapted from:
Morvan Zhou's tutorial page: https://morvanzhou.github.io/tutorials/
Using:
PyTorch: 1.12.1+cpu
gym: 0.19.0
"""
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.distributions import Categorical
# reproducible (this may be an overkill... but it works)
seed=555
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.use_deterministic_algorithms(True)
class PolicyGradient(nn.Module):
def __init__(
self,
n_actions,
n_features,
learning_rate,
gamma, # only for discounting within a game (episode), seems useless
):
super(PolicyGradient, self).__init__()
self.n_actions = n_actions
self.n_features = n_features
self.lr = learning_rate
self.gamma = gamma
# Episode data: actions, rewards:
self.ep_actions = Variable(torch.Tensor())
self.ep_rewards = []
self._build_net()
self.optimizer = optim.Adam(self.parameters(), lr=self.lr)
def net_info(self):
# Total number of params:
total = 0 # **** TO-DO
return ("3,3,5", total)
def _build_net(self):
encoder_layer = nn.TransformerEncoderLayer(d_model=3, nhead=3)
self.trm = nn.TransformerEncoder(encoder_layer, num_layers=5)
self.softmax = nn.Softmax(dim=0)
# W is a 3x9 matrix, to convert 3-vector to 9-vector probability distribution:
self.W = Variable(torch.randn(3, 9), requires_grad=True)
def forward(self, x):
# input dim = n_features = 9 x 3 = 27
# First we need to split the input into 9 parts:
xs = torch.stack(torch.split(x, 3))
# print("xs =", xs)
ys = self.trm(xs) # no need to split results, already in 9x3 chunks
zs = []
for i in range(9):
w = torch.matmul( ys[i], self.W )
zs.append( self.softmax(w) )
# print("zs =", zs)
# *** sum the probability distributions together:
z = torch.stack(zs, dim=1)
u = torch.sum(z, dim=1)
v = self.softmax(u)
# print("v =", v)
return v
# What is the output here? Old output = probs over actions
# The most reasonable output is: probability distribution over actions.
# But there is a waste of 3 dimensions
# Perhaps an even better output format is: probability distribution over specific [x,y]'s.
# Then we need to deal with the problem of merging duplicated [x,y] values.
# The duplicated probabilities could be added or maxed.
# P(A or B) = P(A) + P(B) - P(A and B) but it's difficult to estimate P(A and B)
# Non-monotonic logic is actually more economical as knowledge representation!
# Another question is: if the rule-base suggests action X with probability P1,
# and it also alternatively suggests the same action X with probability P2.
# So it is actually up to us to interpret P1 and P2.
# Naturally, we can normalize all P[i]'s to 1.
# But then X would be chosen with probability P1 + P2.
def choose_action(self, state):
# Select an action (0-8) by running policy model and choosing based on the probabilities in state
state = torch.from_numpy(state).type(torch.FloatTensor)
probs = self(Variable(state))
# probs = 9-dim vector
# print("probs =", probs)
distro = Categorical(probs)
action = distro.sample()
print(action.item(), end='')
# Add log probability of our chosen action to our history
# Unsqueeze(0): tensor (prob, grad_fn) ==> ([prob], grad_fn)
log_prob = distro.log_prob(action).unsqueeze(0)
# print("log prob:", c.log_prob(action))
# print("log prob unsqueezed:", log_prob)
if self.ep_actions.dim() != 0:
self.ep_actions = torch.cat([self.ep_actions, log_prob])
else:
self.ep_actions = (log_prob)
return action
def play_random(self, state, action_space):
# Select an action (0-8) randomly
# NOTE: random player never chooses occupied squares
while True:
action = action_space.sample()
x = action % 3
y = action // 3
occupied = False
for i in range(0, 27, 3): # scan through all 9 propositions, each proposition is a 3-vector
# 'proposition' is a numpy array[3]
proposition = state[i : i + 3]
# print("proposition=",proposition)
if ([x,y,1] == proposition).all():
occupied = True
break
if ([x,y,-1] == proposition).all():
occupied = True
break
if not occupied:
break
return action
def store_transition(self, s, a, r): # state, action, reward
# s is not needed, a is stored during choose_action().
self.ep_rewards.append(r)
def learn(self):
R = 0
rewards = []
# Discount future rewards back to the present using gamma
# print("\nLength of reward episode:", len(self.ep_rewards))
for r in self.ep_rewards[::-1]: # [::-1] reverses a list
R = r + self.gamma * R
rewards.insert(0, R)
# Scale rewards
#if len(rewards) == 1:
#rewards = torch.FloatTensor([-1.0])
#else:
#rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps)
rewards = torch.FloatTensor(rewards)
# Calculate loss
# print("policy history:", self.ep_actions)
# print("rewards:", rewards)
# loss = torch.sum(torch.mul(self.ep_actions, Variable(rewards)).mul(-1), -1)
loss = sum(torch.mul(self.ep_actions, Variable(rewards)).mul(-1), -1)
# Update network weights
self.optimizer.zero_grad()
loss.backward()
self.W.data -= self.lr * self.W.grad.data
self.W.grad.data.zero_() # Manually zero the gradients after updating weights
self.optimizer.step()
# Empty episode data
self.ep_actions = Variable(torch.Tensor())
self.ep_rewards = []
return rewards # == discounted_ep_rewards_norm
def clear_data(self): # empty episode data
self.ep_actions = Variable(torch.Tensor())
self.ep_rewards = []
def save_net(self, fname):
torch.save(self.state_dict(), "PyTorch_models/" + fname + ".dict")
print("Model saved.")
def load_net(self, fname):
torch.load(self.state_dict(), fname + ".dict")
print("Model loaded.")