paddlepaddle
作者：互联网
import gym, os
from itertools import count
import paddle
import paddle.nn as nn
import paddle.optimizer as optim
import paddle.nn.functional as F
from paddle.distribution import Categorical
print(paddle.__version__)
device = paddle.get_device()
env = gym.make("CartPole-v0")
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
lr = 0.001
class Actor(nn.Layer):
def __init__(self, state_size, action_size):
super(Actor, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.linear1 = nn.Linear(self.state_size, 128)
self.linear2 = nn.Linear(128, 256)
self.linear3 = nn.Linear(256, self.action_size)
def forward(self, state):
output = F.relu(self.linear1(state))
output = F.relu(self.linear2(output))
output = self.linear3(output)
distribution = Categorical(F.softmax(output, axis=-1))
return distribution
class Critic(nn.Layer):
def __init__(self, state_size, action_size):
super(Critic, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.linear1 = nn.Linear(self.state_size, 128)
self.linear2 = nn.Linear(128, 256)
self.linear3 = nn.Linear(256, 1)
def forward(self, state):
output = F.relu(self.linear1(state))
output = F.relu(self.linear2(output))
value = self.linear3(output)
return value
def compute_returns(next_value, rewards, masks, gamma=0.99):
R = next_value
returns = []
for step in reversed(range(len(rewards))):
R = rewards[step] + gamma * R * masks[step]
returns.insert(0, R)
return returns
def trainIters(actor, critic, n_iters):
optimizerA = optim.Adam(lr, parameters=actor.parameters())
optimizerC = optim.Adam(lr, parameters=critic.parameters())
for iter in range(n_iters):
state = env.reset()
log_probs = []
values = []
rewards = []
masks = []
entropy = 0
env.reset()
for i in count():
# env.render()
state = paddle.to_tensor(state,dtype="float32",place=device)
dist, value = actor(state), critic(state)
action = dist.sample([1])
next_state, reward, done, _ = env.step(action.cpu().squeeze(0).numpy())
log_prob = dist.log_prob(action);
# entropy += dist.entropy().mean()
log_probs.append(log_prob)
values.append(value)
rewards.append(paddle.to_tensor([reward], dtype="float32", place=device))
masks.append(paddle.to_tensor([1-done], dtype="float32", place=device))
state = next_state
if done:
if iter % 10 == 0:
print('Iteration: {}, Score: {}'.format(iter, i))
break
next_state = paddle.to_tensor(next_state, dtype="float32", place=device)
next_value = critic(next_state)
returns = compute_returns(next_value, rewards, masks)
log_probs = paddle.concat(log_probs)
returns = paddle.concat(returns).detach()
values = paddle.concat(values)
advantage = returns - values
actor_loss = -(log_probs * advantage.detach()).mean()
critic_loss = advantage.pow(2).mean()
optimizerA.clear_grad()
optimizerC.clear_grad()
actor_loss.backward()
critic_loss.backward()
optimizerA.step()
optimizerC.step()
paddle.save(actor.state_dict(), 'model/actor.pdparams')
paddle.save(critic.state_dict(), 'model/critic.pdparams')
env.close()
if __name__ == '__main__':
if os.path.exists('model/actor.pdparams'):
actor = Actor(state_size, action_size)
model_state_dict = paddle.load('model/actor.pdparams')
actor.set_state_dict(model_state_dict )
print('Actor Model loaded')
else:
actor = Actor(state_size, action_size)
if os.path.exists('model/critic.pdparams'):
critic = Critic(state_size, action_size)
model_state_dict = paddle.load('model/critic.pdparams')
critic.set_state_dict(model_state_dict )
print('Critic Model loaded')
else:
critic = Critic(state_size, action_size)
trainIters(actor, critic, n_iters=201)
标签：self,paddlepaddle,critic,paddle,state,action,size 来源： https://blog.csdn.net/antepenultimate/article/details/120890242