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076245e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | import pickle
import random
import time
from collections import deque
import gym_super_mario_bros
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from gym_super_mario_bros.actions import COMPLEX_MOVEMENT
from nes_py.wrappers import JoypadSpace
from wrappers import *
def arrange(s):
if not type(s) == "numpy.ndarray":
s = np.array(s)
assert len(s.shape) == 3
ret = np.transpose(s, (2, 0, 1))
return np.expand_dims(ret, 0)
class replay_memory(object):
def __init__(self, N):
self.memory = deque(maxlen=N)
def push(self, transition):
self.memory.append(transition)
def sample(self, n):
return random.sample(self.memory, n)
def __len__(self):
return len(self.memory)
class model(nn.Module):
def __init__(self, n_frame, n_action, device):
super(model, self).__init__()
self.layer1 = nn.Conv2d(n_frame, 32, 8, 4)
self.layer2 = nn.Conv2d(32, 64, 3, 1)
self.fc = nn.Linear(20736, 512)
self.q = nn.Linear(512, n_action)
self.v = nn.Linear(512, 1)
self.device = device
self.seq = nn.Sequential(self.layer1, self.layer2, self.fc, self.q, self.v)
self.seq.apply(init_weights)
def forward(self, x):
if type(x) != torch.Tensor:
x = torch.FloatTensor(x).to(self.device)
x = torch.relu(self.layer1(x))
x = torch.relu(self.layer2(x))
x = x.view(-1, 20736)
x = torch.relu(self.fc(x))
adv = self.q(x)
v = self.v(x)
q = v + (adv - 1 / adv.shape[-1] * adv.sum(-1, keepdim=True))
return q
def init_weights(m):
if type(m) == nn.Conv2d:
torch.nn.init.xavier_uniform_(m.weight)
m.bias.data.fill_(0.01)
def train(q, q_target, memory, batch_size, gamma, optimizer, device):
s, r, a, s_prime, done = list(map(list, zip(*memory.sample(batch_size))))
s = np.array(s).squeeze()
s_prime = np.array(s_prime).squeeze()
a_max = q(s_prime).max(1)[1].unsqueeze(-1)
r = torch.FloatTensor(r).unsqueeze(-1).to(device)
done = torch.FloatTensor(done).unsqueeze(-1).to(device)
with torch.no_grad():
y = r + gamma * q_target(s_prime).gather(1, a_max) * done
a = torch.tensor(a).unsqueeze(-1).to(device)
q_value = torch.gather(q(s), dim=1, index=a.view(-1, 1).long())
loss = F.smooth_l1_loss(q_value, y).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
return loss
def copy_weights(q, q_target):
q_dict = q.state_dict()
q_target.load_state_dict(q_dict)
def main(env, q, q_target, optimizer, device):
t = 0
gamma = 0.99
batch_size = 256
N = 50000
eps = 0.001
memory = replay_memory(N)
update_interval = 50
print_interval = 10
score_lst = []
total_score = 0.0
loss = 0.0
start_time = time.perf_counter()
for k in range(1000000):
s = arrange(env.reset())
done = False
while not done:
if eps > np.random.rand():
a = env.action_space.sample()
else:
if device == "cpu":
a = np.argmax(q(s).detach().numpy())
else:
a = np.argmax(q(s).cpu().detach().numpy())
s_prime, r, done, _ = env.step(a)
s_prime = arrange(s_prime)
total_score += r
r = np.sign(r) * (np.sqrt(abs(r) + 1) - 1) + 0.001 * r
memory.push((s, float(r), int(a), s_prime, int(1 - done)))
s = s_prime
stage = env.unwrapped._stage
if len(memory) > 2000:
loss += train(q, q_target, memory, batch_size, gamma, optimizer, device)
t += 1
if t % update_interval == 0:
copy_weights(q, q_target)
torch.save(q.state_dict(), "mario_q.pth")
torch.save(q_target.state_dict(), "mario_q_target.pth")
if k % print_interval == 0:
time_spent, start_time = (
time.perf_counter() - start_time,
time.perf_counter(),
)
print(
"%s |Epoch : %d | score : %f | loss : %.2f | stage : %d | time spent: %f"
% (
device,
k,
total_score / print_interval,
loss / print_interval,
stage,
time_spent,
)
)
score_lst.append(total_score / print_interval)
total_score = 0
loss = 0.0
pickle.dump(score_lst, open("score.p", "wb"))
if __name__ == "__main__":
n_frame = 4
env = gym_super_mario_bros.make("SuperMarioBros-v0")
env = JoypadSpace(env, COMPLEX_MOVEMENT)
env = wrap_mario(env)
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
elif torch.backends.mps.is_available():
device = "mps"
q = model(n_frame, env.action_space.n, device).to(device)
q_target = model(n_frame, env.action_space.n, device).to(device)
optimizer = optim.Adam(q.parameters(), lr=0.0001)
print(device)
main(env, q, q_target, optimizer, device)
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