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REINFORCE.py
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REINFORCE.py
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import gym
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical
#Hyperparameters
learning_rate = 0.0002
gamma = 0.98
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.data = []
self.fc1 = nn.Linear(4, 128)
self.fc2 = nn.Linear(128, 2)
self.optimizer = optim.Adam(self.parameters(), lr=learning_rate)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.softmax(self.fc2(x), dim=0)
return x
def put_data(self, item):
self.data.append(item)
def train_net(self):
R = 0
self.optimizer.zero_grad()
for r, prob in self.data[::-1]:
R = r gamma * R
loss = -torch.log(prob) * R
loss.backward()
self.optimizer.step()
self.data = []
def main():
env = gym.make('CartPole-v1')
pi = Policy()
score = 0.0
print_interval = 20
for n_epi in range(10000):
s = env.reset()
done = False
while not done: # CartPole-v1 forced to terminates at 500 step.
prob = pi(torch.from_numpy(s).float())
m = Categorical(prob)
a = m.sample()
s_prime, r, done, info = env.step(a.item())
pi.put_data((r,prob[a]))
s = s_prime
score = r
pi.train_net()
if n_epi%print_interval==0 and n_epi!=0:
print("# of episode :{}, avg score : {}".format(n_epi, score/print_interval))
score = 0.0
env.close()
if __name__ == '__main__':
main()