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PG_symNN.py
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PG_symNN.py
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"""
This is the symNN version, where the state vector is 9 propositions = 9 x 3 = 27-vector
Refer to net_config() below for the current network topology and total number of weights info.
For example: h = (3-9-9) x 9, g = (9-12-9) x 1
Total # weights = (3 * 9 + 9 * 9) * 9 + 9 * 9 + 9 * 9 = 1134
Duplicate weights are counted because they are updated multiple times.
============================================================
Policy Gradient, Reinforcement Learning. Adapted from:
Morvan Zhou's tutorial page: https://morvanzhou.github.io/tutorials/
Using:
PyTorch: 1.9.0+cpu
gym: 0.8.0
"""
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...)
seed=666
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):
config_h = "(3)-10-8"
config_g = "8-(9)"
total = 0
neurons = config_h.split('-')
last_n = 3
for n in neurons[1:]:
n = int(n)
total += last_n * n
last_n = n
total *= 9
neurons = config_g.split('-')
for n in neurons[1:-1]:
n = int(n)
total += last_n * n
last_n = n
total += last_n * 9
return (config_h + 'x' + config_g, total)
def _build_net(self):
# **** h-network, also referred to as "phi" in the literature
# input dim = 3 because each proposition is a 3-vector
self.h1 = nn.Linear(3, 9, bias=True)
self.relu1 = nn.Tanh()
self.h2 = nn.Linear(9, 9, bias=True)
self.relu2 = nn.Tanh()
# **** g-network, also referred to as "rho" in the literature
# input dim can be arbitrary, here chosen to be n_actions
self.g1 = nn.Linear(9, 9, bias=True)
self.relu3 = nn.Tanh()
# output dim must be n_actions
self.g2 = nn.Linear(9, self.n_actions, bias=True)
self.softmax = nn.Softmax(dim=0)
def forward(self, x):
# input dim = n_features = 9 x 3 = 27
# there are 9 h-networks each taking a dim-3 vector input
# First we need to split the input into 9 parts:
xs = torch.split(x, 3)
# h-network:
ys = []
for i in range(9): # repeat h1 9 times
ys.append( self.relu1( self.h1(xs[i]) ))
zs = []
for i in range(9): # repeat h2 9 times
zs.append( self.relu2( self.h2(ys[i]) ))
# add all the z's together:
z = torch.stack(zs, dim=1)
z = torch.sum(z, dim=1)
# g-network:
z1 = self.g1(z)
z1 = self.relu3(z1)
z2 = self.g2(z1)
z2 = self.softmax(z2)
return z2
def choose_action(self, state):
#Select an action (0-9) by running policy model and choosing based on the probabilities in state
state = torch.from_numpy(state).type(torch.FloatTensor)
probs = self(Variable(state))
c = Categorical(probs)
action = c.sample()
# Add log probability of our chosen action to our history
# Unsqueeze: returns a new tensor with a dimension of size 1 inserted at the specified position.
# Unsqueeze(0): tensor (prob, grad_fn) ==> ([prob], grad_fn)
log_prob = c.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 (0-9) 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 = torch.FloatTensor(rewards)
#rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps)
# print(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.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.")