import gym
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from spikingjelly.activation_based.examples.common.multiprocessing_env import SubprocVecEnv
# Use CUDA
use_cuda = torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
# Set Seed
seed = 1
random.seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
# Create Environments
num_envs = 4
env_name = 'CartPole-v0'
[文档]def make_env():
def _thunk():
env = gym.make(env_name)
env.seed(seed)
return env
return _thunk
# Neural Network
[文档]class ActorCritic(nn.Module):
def __init__(self, num_inputs, num_outputs, hidden_size, std=0.0):
super(ActorCritic, self).__init__()
self.critic = nn.Sequential(
nn.Linear(num_inputs, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, 1)
)
self.actor = nn.Sequential(
nn.Linear(num_inputs, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, num_outputs),
)
self.log_std = nn.Parameter(torch.ones(1, num_outputs) * std)
[文档] def forward(self, x):
value = self.critic(x)
mu = self.actor(x)
std = self.log_std.exp().expand_as(mu)
dist = Normal(mu, std)
return dist, value
if __name__ == '__main__':
envs = [make_env() for i in range(num_envs)]
envs = SubprocVecEnv(envs)
env = gym.make(env_name)
env.seed(seed)
def test_env(vis=False):
state = env.reset()
if vis: env.render()
done = False
total_reward = 0
while not done:
state = torch.FloatTensor(state).unsqueeze(0).to(device)
dist, _ = model(state)
next_state, reward, done, _ = env.step(torch.max(dist.sample(), 1)[1].cpu().numpy()[0])
state = next_state
if vis: env.render()
total_reward += reward
return total_reward
# GAE
def compute_gae(next_value, rewards, masks, values, gamma=0.99, tau=0.95):
values = values + [next_value]
gae = 0
returns = []
for step in reversed(range(len(rewards))):
delta = rewards[step] + gamma * values[step + 1] * masks[step] - values[step]
gae = delta + gamma * tau * masks[step] * gae
returns.insert(0, gae + values[step])
return returns
# Proximal Policy Optimization Algorithm
# Arxiv: "https://arxiv.org/abs/1707.06347"
def ppo_iter(mini_batch_size, states, actions, log_probs, returns, advantage):
batch_size = states.size(0)
ids = np.random.permutation(batch_size)
ids = np.split(ids[:batch_size // mini_batch_size * mini_batch_size], batch_size // mini_batch_size)
for i in range(len(ids)):
yield states[ids[i], :], actions[ids[i], :], log_probs[ids[i], :], returns[ids[i], :], advantage[ids[i], :]
def ppo_update(ppo_epochs, mini_batch_size, states, actions, log_probs, returns, advantages, clip_param=0.2):
for _ in range(ppo_epochs):
for state, action, old_log_probs, return_, advantage in ppo_iter(mini_batch_size, states, actions, log_probs, returns, advantages):
dist, value = model(state)
entropy = dist.entropy().mean()
new_log_probs = dist.log_prob(action)
ratio = (new_log_probs - old_log_probs).exp()
surr1 = ratio * advantage
surr2 = torch.clamp(ratio, 1.0 - clip_param, 1.0 + clip_param) * advantage
actor_loss = - torch.min(surr1, surr2).mean()
critic_loss = (return_ - value).pow(2).mean()
loss = 0.5 * critic_loss + actor_loss - 0.001 * entropy
optimizer.zero_grad()
loss.backward()
optimizer.step()
num_inputs = envs.observation_space.shape[0]
num_outputs = env.action_space.n
print('State Num: %d, Action Num: %d' % (num_inputs, num_outputs))
# Hyper params:
hidden_size = 32
lr = 1e-3
num_steps = 128
mini_batch_size = 256
ppo_epochs = 30
model = ActorCritic(num_inputs, num_outputs, hidden_size).to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
max_steps = 10000
step_idx = 0
state = envs.reset()
writer = SummaryWriter(logdir='./log')
while step_idx < max_steps:
log_probs = []
values = []
states = []
actions = []
rewards = []
masks = []
entropy = 0
for _ in range(num_steps):
state = torch.FloatTensor(state).to(device)
dist, value = model(state)
action = dist.sample()
next_state, reward, done, _ = envs.step(torch.max(action, 1)[1].cpu().numpy())
log_prob = dist.log_prob(action)
entropy += dist.entropy().mean()
log_probs.append(log_prob)
values.append(value)
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
states.append(state)
actions.append(action)
state = next_state
step_idx += 1
if step_idx % 100 == 0:
test_reward = test_env()
print('Step: %d, Reward: %.2f' % (step_idx, test_reward))
writer.add_scalar('PPO-' + env_name + '/Reward', test_reward, step_idx)
next_state = torch.FloatTensor(next_state).to(device)
_, next_value = model(next_state)
returns = compute_gae(next_value, rewards, masks, values)
returns = torch.cat(returns).detach()
log_probs = torch.cat(log_probs).detach()
values = torch.cat(values).detach()
states = torch.cat(states)
actions = torch.cat(actions)
advantage = returns - values
ppo_update(ppo_epochs, mini_batch_size, states, actions, log_probs, returns, advantage)
print('----------------------------')
print('Complete')
writer.close()