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import argparse
import os
import random
import time
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from stable_baselines3.common.atari_wrappers import (
ClipRewardEnv,
EpisodicLifeEnv,
FireResetEnv,
MaxAndSkipEnv,
NoopResetEnv
)
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py'))
parser.add_argument('--gym_id', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--learning_rate', type=float, default=2.5e-4)
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--total_steps', type=int, default=int(1e7))
parser.add_argument('--use_cuda', type=bool, default=True)
parser.add_argument('--num_envs', type=int, default=8)
parser.add_argument('--num_steps', type=int, default=128)
parser.add_argument('--lr_decay', type=bool, default=True)
parser.add_argument('--use_gae', type=bool, default=True)
parser.add_argument('--gae_lambda', type=float, default=0.95)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--num_mini_batches', type=int, default=4)
parser.add_argument('--update_epochs', type=int, default=4)
parser.add_argument('--norm_adv', type=bool, default=True)
parser.add_argument('--clip_value_loss', type=bool, default=True)
parser.add_argument('--c_1', type=float, default=1.0)
parser.add_argument('--c_2', type=float, default=0.01)
parser.add_argument('--max_grad_norm', type=float, default=0.5)
parser.add_argument('--clip_epsilon', type=float, default=0.2)
a = parser.parse_args()
a.batch_size = int(a.num_envs * a.num_steps)
a.minibatch_size = int(a.batch_size // a.num_mini_batches)
return a
def make_env(gym_id, seed):
def thunk():
env = gym.make(gym_id)
env = gym.wrappers.RecordEpisodeStatistics(env)
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = ClipRewardEnv(env)
env = gym.wrappers.ResizeObservation(env, (84, 84))
env = gym.wrappers.GrayScaleObservation(env)
env = gym.wrappers.FrameStack(env, 4)
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env
return thunk
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer
class Agent(nn.Module):
def __init__(self, e):
super(Agent, self).__init__()
self.network = nn.Sequential(
layer_init(nn.Conv2d(4, 32, 8, stride=4)),
nn.ReLU(),
layer_init(nn.Conv2d(32, 64, 4, stride=2)),
nn.ReLU(),
layer_init(nn.Conv2d(64, 64, 3, stride=1)),
nn.ReLU(),
nn.Flatten(),
layer_init(nn.Linear(64 * 7 * 7, 512)),
nn.ReLU(),
)
self.actor = layer_init(nn.Linear(512, e.single_action_space.n), std=0.01)
self.critic = layer_init(nn.Linear(512, 1), std=1)
def get_value(self, x):
return self.critic(self.network(x / 255.0))
def get_action_and_value(self, x, a=None, show_all=False):
hidden = self.network(x / 255.0)
log = self.actor(hidden)
p = Categorical(logits=log)
if a is None:
a = p.sample()
if show_all:
return a, p.log_prob(a), p.entropy(), self.critic(hidden), p.probs
return a, p.log_prob(a), p.entropy(), self.critic(hidden)
def main(env_id, seed):
args = get_args()
args.gym_id = env_id
args.seed = seed
run_name = (
'ppo' +
'_epoch_' + str(args.update_epochs) +
'_seed_' + str(args.seed)
)
# 保存训练日志
path_string = str(args.gym_id).split('NoFrameskip')[0] + '/' + run_name
writer = SummaryWriter(path_string)
writer.add_text(
'Hyperparameter',
'|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])),
)
# 随机数种子
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# 初始化环境
device = torch.device('cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu')
envs = gym.vector.SyncVectorEnv(
[make_env(args.gym_id, args.seed + i) for i in range(args.num_envs)]
)
assert isinstance(envs.single_action_space, gym.spaces.Discrete), 'only discrete action space is supported'
agent = Agent(envs).to(device)
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
# 初始化存储
obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
probs = torch.zeros((args.num_steps, args.num_envs, envs.single_action_space.n)).to(device)
log_probs = torch.zeros((args.num_steps, args.num_envs)).to(device)
rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
values = torch.zeros((args.num_steps, args.num_envs)).to(device)
# 开始收集数据
global_step = 0
start_time = time.time()
next_obs = torch.Tensor(envs.reset()).to(device)
next_done = torch.zeros(args.num_envs).to(device)
num_updates = int(args.total_steps // args.batch_size)
for update in tqdm(range(1, num_updates + 1)):
# 学习率是否衰减
if args.lr_decay:
frac = 1.0 - (update - 1.0) / num_updates
lr_now = frac * args.learning_rate
optimizer.param_groups[0]['lr'] = lr_now
for step in range(0, args.num_steps):
# 每一步更新步数还要乘上并行的环境数
global_step += 1 * args.num_envs
obs[step] = next_obs
dones[step] = next_done
# 计算旧的策略网络输出动作概率分布的对数
with torch.no_grad():
action, log_prob, _, value, prob = agent.get_action_and_value(next_obs, show_all=True)
values[step] = value.flatten()
actions[step] = action
probs[step] = prob
log_probs[step] = log_prob
# 更新环境
next_obs, reward, done, info = envs.step(action.cpu().numpy())
rewards[step] = torch.tensor(reward).to(device).view(-1)
next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
# 如果并行环境中有一个环境结束了，则输出总步数以及回合奖励和回合长度
for item in info:
if 'episode' in item.keys():
# print(f"global_step={global_step}, episodic_return={item['episode']['r']}")
writer.add_scalar('charts/episodic_return', item['episode']['r'], global_step)
break
# 计算GAE
with torch.no_grad():
next_value = agent.get_value(next_obs).reshape(1, -1)
if args.use_gae:
advantages = torch.zeros_like(rewards).to(device)
last_gae_lam = 0
for t in reversed(range(args.num_steps)):
if t == args.num_steps - 1:
next_non_terminal = 1.0 - next_done
next_values = next_value
else:
next_non_terminal = 1.0 - dones[t + 1]
next_values = values[t + 1]
delta = rewards[t] + args.gamma * next_values * next_non_terminal - values[t]
advantages[t] = last_gae_lam = (
delta + args.gamma * args.gae_lambda * next_non_terminal * last_gae_lam
)
returns = advantages + values
else:
returns = torch.zeros_like(rewards).to(device)
for t in reversed(range(args.num_steps)):
if t == args.num_steps - 1:
next_non_terminal = 1.0 - next_done
next_return = next_value
else:
next_non_terminal = 1.0 - dones[t + 1]
next_return = returns[t + 1]
returns[t] = rewards[t] + args.gamma * next_non_terminal * next_return
advantages = returns - values
# ------------------------------- 上面收集了足够的数据，下面开始更新 ------------------------------- #
# 将每个batch展平
b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
b_probs = probs.reshape((-1, envs.single_action_space.n))
b_log_probs = log_probs.reshape(-1)
b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
b_advantages = advantages.reshape(-1)
b_returns = returns.reshape(-1)
b_values = values.reshape(-1)
# 更新策略网络和价值网络
b_index = np.arange(args.batch_size)
for epoch in range(1, args.update_epochs + 1):
np.random.shuffle(b_index)
for start in range(0, args.batch_size, args.minibatch_size):
end = start + args.minibatch_size
mb_index = b_index[start:end]
# 得到最新的策略网络和价值网络输出
_, new_log_prob, entropy, new_value, new_probs = (
agent.get_action_and_value(b_obs[mb_index], b_actions.long()[mb_index], show_all=True)
)
# 计算kl散度
d = torch.sum(
b_probs[mb_index] * torch.log((b_probs[mb_index] + 1e-12) / (new_probs + 1e-12)), 1
)
writer.add_scalar('charts/average_kld', d.mean(), global_step)
writer.add_scalar('others/min_kld', d.min(), global_step)
writer.add_scalar('others/max_kld', d.max(), global_step)
log_ratio = new_log_prob - b_log_probs[mb_index]
ratio = log_ratio.exp()
# 优势值归一化
mb_advantages = b_advantages[mb_index]
if args.norm_adv:
mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-12)
# 策略网络损失
pg_loss1 = -mb_advantages * ratio
pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_epsilon, 1 + args.clip_epsilon)
pg_loss = torch.max(pg_loss1, pg_loss2).mean()
# 价值网络损失
new_value = new_value.view(-1)
if args.clip_value_loss:
v_loss_un_clipped = (new_value - b_returns[mb_index]) ** 2
v_clipped = b_values[mb_index] + torch.clamp(
new_value - b_values[mb_index],
-args.clip_epsilon,
args.clip_epsilon,
)
v_loss_clipped = (v_clipped - b_returns[mb_index]) ** 2
v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped)
v_loss = 0.5 * v_loss_max.mean()
else:
v_loss = 0.5 * ((new_value - b_returns[mb_index]) ** 2).mean()
entropy_loss = entropy.mean()
# 损失
loss = pg_loss + v_loss * args.c_1 - entropy_loss * args.c_2
# 写入训练过程的一些数据
writer.add_scalar('losses/value_loss', v_loss.item(), global_step)
writer.add_scalar('losses/policy_loss', pg_loss.item(), global_step)
writer.add_scalar('losses/entropy', entropy_loss.item(), global_step)
writer.add_scalar('losses/delta', torch.abs(ratio - 1).mean().item(), global_step)
# 更新网络参数
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
optimizer.step()
y_pre, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
var_y = np.var(y_true)
explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pre) / var_y
# 写入训练过程的一些数据
writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step)
writer.add_scalar('others/explained_variance', explained_var, global_step)
writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step)
envs.close()
writer.close()
def run():
for env_id in ['Breakout']:
for seed in [1, 2, 3]:
print(env_id + 'NoFrameskip-v4', 'seed:', seed)
main(env_id + 'NoFrameskip-v4', seed)
if __name__ == '__main__':
run()

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import argparse
import os
import random
import time
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from stable_baselines3.common.atari_wrappers import (
ClipRewardEnv,
EpisodicLifeEnv,
FireResetEnv,
MaxAndSkipEnv,
NoopResetEnv
)
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py'))
parser.add_argument('--gym_id', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--learning_rate', type=float, default=2.5e-4)
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--total_steps', type=int, default=int(1e7))
parser.add_argument('--use_cuda', type=bool, default=True)
parser.add_argument('--num_envs', type=int, default=8)
parser.add_argument('--num_steps', type=int, default=128)
parser.add_argument('--lr_decay', type=bool, default=True)
parser.add_argument('--use_gae', type=bool, default=True)
parser.add_argument('--gae_lambda', type=float, default=0.95)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--num_mini_batches', type=int, default=4)
parser.add_argument('--update_epochs', type=int, default=8)
parser.add_argument('--norm_adv', type=bool, default=True)
parser.add_argument('--clip_value_loss', type=bool, default=True)
parser.add_argument('--c_1', type=float, default=1.0)
parser.add_argument('--c_2', type=float, default=0.01)
parser.add_argument('--max_grad_norm', type=float, default=0.5)
parser.add_argument('--kld_max', type=float, default=0.02)
a = parser.parse_args()
a.batch_size = int(a.num_envs * a.num_steps)
a.minibatch_size = int(a.batch_size // a.num_mini_batches)
return a
def make_env(gym_id, seed):
def thunk():
env = gym.make(gym_id)
env = gym.wrappers.RecordEpisodeStatistics(env)
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = ClipRewardEnv(env)
env = gym.wrappers.ResizeObservation(env, (84, 84))
env = gym.wrappers.GrayScaleObservation(env)
env = gym.wrappers.FrameStack(env, 4)
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env
return thunk
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer
class Agent(nn.Module):
def __init__(self, e):
super(Agent, self).__init__()
self.network = nn.Sequential(
layer_init(nn.Conv2d(4, 32, 8, stride=4)),
nn.ReLU(),
layer_init(nn.Conv2d(32, 64, 4, stride=2)),
nn.ReLU(),
layer_init(nn.Conv2d(64, 64, 3, stride=1)),
nn.ReLU(),
nn.Flatten(),
layer_init(nn.Linear(64 * 7 * 7, 512)),
nn.ReLU(),
)
self.actor = layer_init(nn.Linear(512, e.single_action_space.n), std=0.01)
self.critic = layer_init(nn.Linear(512, 1), std=1)
def get_value(self, x):
return self.critic(self.network(x / 255.0))
def get_action_and_value(self, x, a=None, show_all=False):
hidden = self.network(x / 255.0)
log = self.actor(hidden)
p = Categorical(logits=log)
if a is None:
a = p.sample()
if show_all:
return a, p.log_prob(a), p.entropy(), self.critic(hidden), p.probs
return a, p.log_prob(a), p.entropy(), self.critic(hidden)
def main(env_id, seed):
args = get_args()
args.gym_id = env_id
args.seed = seed
run_name = (
'spo_' + str(args.kld_max) +
'_epoch_' + str(args.update_epochs) +
'_seed_' + str(args.seed)
)
# 保存训练日志
path_string = str(args.gym_id).split('NoFrameskip')[0] + '/' + run_name
writer = SummaryWriter(path_string)
writer.add_text(
'Hyperparameter',
'|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])),
)
# 随机数种子
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# 初始化环境
device = torch.device('cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu')
envs = gym.vector.SyncVectorEnv(
[make_env(args.gym_id, args.seed + i) for i in range(args.num_envs)]
)
assert isinstance(envs.single_action_space, gym.spaces.Discrete), 'only discrete action space is supported'
agent = Agent(envs).to(device)
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
# 初始化存储
obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
probs = torch.zeros((args.num_steps, args.num_envs, envs.single_action_space.n)).to(device)
log_probs = torch.zeros((args.num_steps, args.num_envs)).to(device)
rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
values = torch.zeros((args.num_steps, args.num_envs)).to(device)
# 开始收集数据
global_step = 0
start_time = time.time()
next_obs = torch.Tensor(envs.reset()).to(device)
next_done = torch.zeros(args.num_envs).to(device)
num_updates = int(args.total_steps // args.batch_size)
for update in tqdm(range(1, num_updates + 1)):
# 学习率是否衰减
if args.lr_decay:
frac = 1.0 - (update - 1.0) / num_updates
lr_now = frac * args.learning_rate
optimizer.param_groups[0]['lr'] = lr_now
for step in range(0, args.num_steps):
# 每一步更新步数还要乘上并行的环境数
global_step += 1 * args.num_envs
obs[step] = next_obs
dones[step] = next_done
# 计算旧的策略网络输出动作概率分布的对数
with torch.no_grad():
action, log_prob, _, value, prob = agent.get_action_and_value(next_obs, show_all=True)
values[step] = value.flatten()
actions[step] = action
probs[step] = prob
log_probs[step] = log_prob
# 更新环境
next_obs, reward, done, info = envs.step(action.cpu().numpy())
rewards[step] = torch.tensor(reward).to(device).view(-1)
next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
# 如果并行环境中有一个环境结束了，则输出总步数以及回合奖励和回合长度
for item in info:
if 'episode' in item.keys():
# print(f"global_step={global_step}, episodic_return={item['episode']['r']}")
writer.add_scalar('charts/episodic_return', item['episode']['r'], global_step)
break
# 计算GAE
with torch.no_grad():
next_value = agent.get_value(next_obs).reshape(1, -1)
if args.use_gae:
advantages = torch.zeros_like(rewards).to(device)
last_gae_lam = 0
for t in reversed(range(args.num_steps)):
if t == args.num_steps - 1:
next_non_terminal = 1.0 - next_done
next_values = next_value
else:
next_non_terminal = 1.0 - dones[t + 1]
next_values = values[t + 1]
delta = rewards[t] + args.gamma * next_values * next_non_terminal - values[t]
advantages[t] = last_gae_lam = (
delta + args.gamma * args.gae_lambda * next_non_terminal * last_gae_lam
)
returns = advantages + values
else:
returns = torch.zeros_like(rewards).to(device)
for t in reversed(range(args.num_steps)):
if t == args.num_steps - 1:
next_non_terminal = 1.0 - next_done
next_return = next_value
else:
next_non_terminal = 1.0 - dones[t + 1]
next_return = returns[t + 1]
returns[t] = rewards[t] + args.gamma * next_non_terminal * next_return
advantages = returns - values
# ------------------------------- 上面收集了足够的数据，下面开始更新 ------------------------------- #
# 将每个batch展平
b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
b_probs = probs.reshape((-1, envs.single_action_space.n))
b_log_probs = log_probs.reshape(-1)
b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
b_advantages = advantages.reshape(-1)
b_returns = returns.reshape(-1)
b_values = values.reshape(-1)
# 更新策略网络和价值网络
b_index = np.arange(args.batch_size)
for epoch in range(1, args.update_epochs + 1):
np.random.shuffle(b_index)
t = 0
for start in range(0, args.batch_size, args.minibatch_size):
t += 1
end = start + args.minibatch_size
mb_index = b_index[start:end]
# 得到最新的策略网络和价值网络输出
_, new_log_prob, entropy, new_value, new_probs = (
agent.get_action_and_value(b_obs[mb_index], b_actions.long()[mb_index], show_all=True)
)
# 计算kl散度
d = torch.sum(
b_probs[mb_index] * torch.log((b_probs[mb_index] + 1e-12) / (new_probs + 1e-12)), 1
)
writer.add_scalar('charts/average_kld', d.mean(), global_step)
writer.add_scalar('others/min_kld', d.min(), global_step)
writer.add_scalar('others/max_kld', d.max(), global_step)
log_ratio = new_log_prob - b_log_probs[mb_index]
ratios = log_ratio.exp()
# 优势值归一化
mb_advantages = b_advantages[mb_index]
if args.norm_adv:
mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-12)
# 策略网络损失
new_value = new_value.view(-1)
if epoch == 1 and t == 1:
pg_loss = (-mb_advantages * ratios).mean()
else:
d_clip = torch.clamp(input=d, min=0, max=args.kld_max)
# d_clip / d
ratio = d_clip / (d + 1e-12)
# sign_a
sign_a = torch.sign(mb_advantages)
# (d_clip / d + sign_a - 1) * sign_a
result = (ratio + sign_a - 1) * sign_a
# 策略网络损失
pg_loss = (-mb_advantages * ratios * result).mean()
# 价值网络损失
new_value = new_value.view(-1)
if args.clip_value_loss:
v_loss_un_clipped = (new_value - b_returns[mb_index]) ** 2
v_clipped = b_values[mb_index] + torch.clamp(
new_value - b_values[mb_index],
-0.2,
0.2,
)
v_loss_clipped = (v_clipped - b_returns[mb_index]) ** 2
v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped)
v_loss = 0.5 * v_loss_max.mean()
else:
v_loss = 0.5 * ((new_value - b_returns[mb_index]) ** 2).mean()
entropy_loss = entropy.mean()
# 损失
loss = pg_loss + v_loss * args.c_1 - entropy_loss * args.c_2
# 写入训练过程的一些数据
writer.add_scalar('losses/value_loss', v_loss.item(), global_step)
writer.add_scalar('losses/policy_loss', pg_loss.item(), global_step)
writer.add_scalar('losses/entropy', entropy_loss.item(), global_step)
writer.add_scalar('losses/delta', torch.abs(ratios - 1).mean().item(), global_step)
# 更新网络参数
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
optimizer.step()
y_pre, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
var_y = np.var(y_true)
explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pre) / var_y
# 写入训练过程的一些数据
writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step)
writer.add_scalar('others/explained_variance', explained_var, global_step)
writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step)
envs.close()
writer.close()
def run():
for env_id in ['Breakout']:
for seed in [1, 2, 3]:
print(env_id + 'NoFrameskip-v4', 'seed:', seed)
main(env_id + 'NoFrameskip-v4', seed)
if __name__ == '__main__':
run()

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import argparse
import os
import time
import gymnasium as gym
import numpy as np
import torch
from stable_baselines3.common.atari_wrappers import FireResetEnv, EpisodicLifeEnv, ClipRewardEnv
from torch import nn, optim
from torch.distributions import Categorical
from torch.nn.utils.clip_grad import clip_grad_norm_
from torch.utils.tensorboard.writer import SummaryWriter
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py'))
parser.add_argument('--env_id', type=str, default='ALE/Breakout-v5')
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--use_cuda', type=bool, default=True)
parser.add_argument('--learning_rate', type=float, default=2.5e-4)
parser.add_argument('--lr_decay', type=bool, default=True)
parser.add_argument('--total_steps', type=int, default=int(1e7))
parser.add_argument('--num_envs', type=int, default=8)
parser.add_argument('--num_steps', type=int, default=128)
parser.add_argument('--update_epochs', type=int, default=8)
parser.add_argument('--num_mini_batches', type=int, default=4)
parser.add_argument('--gae_lambda', type=float, default=0.95)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--clip_value_loss', type=bool, default=True)
parser.add_argument('--c_1', type=float, default=1.0)
parser.add_argument('--c_2', type=float, default=0.01)
parser.add_argument('--clip_grad_norm', type=float, default=0.5)
parser.add_argument('--clip_epsilon', type=float, default=0.2)
args = parser.parse_args()
args.device = torch.device('cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu')
args.batch_size = int(args.num_envs * args.num_steps)
args.minibatch_size = int(args.batch_size // args.num_mini_batches)
args.num_updates = int(args.total_steps // args.batch_size)
return args
def make_env(env_id):
def thunk():
env = gym.make(env_id, frameskip=1, repeat_action_probability=0.0, full_action_space=False)
env = gym.wrappers.RecordEpisodeStatistics(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = EpisodicLifeEnv(env)
env = ClipRewardEnv(env)
env = gym.wrappers.AtariPreprocessing(env, scale_obs=True)
env = gym.wrappers.FrameStack(env, 4)
return env
return thunk
def compute_advantages(rewards, flags, values, last_value, args):
advantages = torch.zeros((args.num_steps, args.num_envs)).to(args.device)
adv = torch.zeros(args.num_envs).to(args.device)
for i in reversed(range(args.num_steps)):
returns = rewards[i] + args.gamma * flags[i] * last_value
delta = returns - values[i]
adv = delta + args.gamma * args.gae_lambda * flags[i] * adv
advantages[i] = adv
last_value = values[i]
return advantages
class Buffer:
def __init__(self, num_steps, num_envs, observation_shape, action_dim, device):
self.states = np.zeros((num_steps, num_envs, *observation_shape), dtype=np.float32)
self.actions = np.zeros((num_steps, num_envs), dtype=np.int64)
self.rewards = np.zeros((num_steps, num_envs), dtype=np.float32)
self.flags = np.zeros((num_steps, num_envs), dtype=np.float32)
self.log_probs = np.zeros((num_steps, num_envs), dtype=np.float32)
self.probs = np.zeros((num_steps, num_envs, action_dim), dtype=np.float32)
self.values = np.zeros((num_steps, num_envs), dtype=np.float32)
self.step = 0
self.num_steps = num_steps
self.device = device
def push(self, state, action, reward, flag, log_prob, prob, value):
self.states[self.step] = state
self.actions[self.step] = action
self.rewards[self.step] = reward
self.flags[self.step] = flag
self.log_probs[self.step] = log_prob
self.probs[self.step] = prob
self.values[self.step] = value
self.step = (self.step + 1) % self.num_steps
def get(self):
return (
torch.from_numpy(self.states).to(self.device),
torch.from_numpy(self.actions).to(self.device),
torch.from_numpy(self.rewards).to(self.device),
torch.from_numpy(self.flags).to(self.device),
torch.from_numpy(self.log_probs).to(self.device),
torch.from_numpy(self.values).to(self.device),
)
def get_probs(self):
return torch.from_numpy(self.probs).to(self.device)
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer
class Agent(nn.Module):
def __init__(self, action_dim, device):
super().__init__()
self.encoder = nn.Sequential(
layer_init(nn.Conv2d(4, 32, 8, stride=4)),
nn.ReLU(),
layer_init(nn.Conv2d(32, 64, 4, stride=2)),
nn.ReLU(),
layer_init(nn.Conv2d(64, 64, 3, stride=1)),
nn.ReLU(),
nn.Flatten(),
layer_init(nn.Linear(64 * 7 * 7, 512)),
nn.ReLU()
)
self.actor_net = layer_init(nn.Linear(512, action_dim), std=0.01)
self.critic_net = layer_init(nn.Linear(512, 1), std=1)
if device.type == 'cuda':
self.cuda()
def forward(self, state):
hidden = self.encoder(state)
actor_value = self.actor_net(hidden)
distribution = Categorical(logits=actor_value)
action = distribution.sample()
log_prob = distribution.log_prob(action)
value = self.critic_net(hidden).squeeze(-1)
return action, log_prob, value, distribution.probs
def evaluate(self, states, actions):
hidden = self.encoder(states)
actor_values = self.actor_net(hidden)
distribution = Categorical(logits=actor_values)
log_probs = distribution.log_prob(actions)
entropy = distribution.entropy()
values = self.critic_net(hidden).squeeze(-1)
return log_probs, values, entropy, distribution.probs
def critic(self, state):
return self.critic_net(self.encoder(state)).squeeze(-1)
def train(env_id, seed):
args = get_args()
args.env_id = env_id
args.seed = seed
run_name = (
'ppo_clip' +
'_epoch_' + str(args.update_epochs) +
'_seed_' + str(args.seed)
)
# 保存训练日志
path_string = str(args.env_id)[4:] + '/' + run_name
writer = SummaryWriter(path_string)
writer.add_text(
'Hyperparameter',
'|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])),
)
# 初始化并行环境
envs = gym.vector.AsyncVectorEnv([make_env(args.env_id) for _ in range(args.num_envs)])
# 状态空间和动作空间
observation_shape = envs.single_observation_space.shape
action_dim = envs.single_action_space.n
# 随机数种子
if args.seed:
numpy_rng = np.random.default_rng(args.seed)
torch.manual_seed(args.seed)
state, _ = envs.reset(seed=args.seed)
else:
numpy_rng = np.random.default_rng()
state, _ = envs.reset()
# 价值网络和策略网络
agent = Agent(action_dim, args.device)
# 优化器
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate)
# 存储数据的buffer
rollout_buffer = Buffer(args.num_steps, args.num_envs, observation_shape, action_dim, args.device)
global_step = 0
start_time = time.time()
# 开始收集数据
for _ in tqdm(range(args.num_updates)):
# 学习率线性递减
if args.lr_decay:
optimizer.param_groups[0]['lr'] -= (args.learning_rate - 1e-12) / args.num_updates
for _ in range(args.num_steps):
global_step += 1 * args.num_envs
with torch.no_grad():
action, log_prob, value, prob = agent(torch.from_numpy(state).to(args.device).float())
action = action.cpu().numpy()
next_state, reward, terminated, truncated, all_info = envs.step(action)
# 存储数据
flag = 1.0 - np.logical_or(terminated, truncated)
log_prob = log_prob.cpu().numpy()
prob = prob.cpu().numpy()
value = value.cpu().numpy()
rollout_buffer.push(state, action, reward, flag, log_prob, prob, value)
state = next_state
if 'final_info' not in all_info:
continue
# 写入训练过程的数据
for info in all_info['final_info']:
if info is None:
continue
if 'episode' in info.keys():
writer.add_scalar('charts/episodic_return', info['episode']['r'], global_step)
# print(float(info['episode']['r']))
break
# ------------------------------- 上面收集了足够的数据，下面开始更新 ------------------------------- #
states, actions, rewards, flags, log_probs, values = rollout_buffer.get()
probs = rollout_buffer.get_probs()
with torch.no_grad():
last_value = agent.critic(torch.from_numpy(next_state).to(args.device).float())
# 计算优势值和TD目标
advantages = compute_advantages(rewards, flags, values, last_value, args)
td_target = advantages + values
# 将数据展平
states = states.reshape(-1, *observation_shape)
actions = actions.reshape(-1)
log_probs = log_probs.reshape(-1)
probs = probs.reshape((-1, action_dim))
td_target = td_target.reshape(-1)
advantages = advantages.reshape(-1)
values = values.reshape(-1)
batch_indexes = np.arange(args.batch_size)
# 更新策略网络和价值网络
for e in range(1, args.update_epochs + 1):
numpy_rng.shuffle(batch_indexes)
for start in range(0, args.batch_size, args.minibatch_size):
end = start + args.minibatch_size
index = batch_indexes[start:end]
# 得到最新的策略网络和价值网络输出
new_log_probs, td_predict, entropy, new_probs = agent.evaluate(states[index], actions[index])
log_ratio = new_log_probs - log_probs[index]
ratios = log_ratio.exp()
# 计算kl散度
d = torch.sum(
probs[index] * torch.log((probs[index] + 1e-12) / (new_probs + 1e-12)), 1
)
writer.add_scalar('charts/average_kld', d.mean(), global_step)
writer.add_scalar('others/min_kld', d.min(), global_step)
writer.add_scalar('others/max_kld', d.max(), global_step)
# 优势值标准化
b_advantages = advantages[index]
b_advantages = (b_advantages - b_advantages.mean()) / (b_advantages.std() + 1e-12)
# 策略网络损失
policy_loss_1 = b_advantages * ratios
policy_loss_2 = b_advantages * torch.clamp(
ratios, 1.0 - args.clip_epsilon, 1.0 + args.clip_epsilon
)
policy_loss = -torch.min(policy_loss_1, policy_loss_2).mean()
# 价值网络损失
if args.clip_value_loss:
v_loss_un_clipped = (td_predict - td_target[index]) ** 2
v_clipped = td_target[index] + torch.clamp(
td_predict - td_target[index],
-args.clip_epsilon,
args.clip_epsilon,
)
v_loss_clipped = (v_clipped - td_target[index]) ** 2
v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped)
value_loss = 0.5 * v_loss_max.mean()
else:
value_loss = 0.5 * ((td_predict - td_target[index]) ** 2).mean()
entropy_loss = entropy.mean()
# 保存训练过程中的一些数据
writer.add_scalar('losses/value_loss', value_loss.item(), global_step)
writer.add_scalar('losses/policy_loss', policy_loss.item(), global_step)
writer.add_scalar('losses/entropy', entropy_loss.item(), global_step)
writer.add_scalar('losses/delta', torch.abs(ratios - 1).mean().item(), global_step)
# 总的损失
loss = policy_loss + value_loss * args.c_1 - entropy_loss * args.c_2
# 更新网络参数
optimizer.zero_grad()
loss.backward()
clip_grad_norm_(agent.parameters(), args.clip_grad_norm)
optimizer.step()
explained_var = (
np.nan if torch.var(td_target) == 0 else 1 - torch.var(td_target - values) / torch.var(td_target)
)
writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step)
writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step)
writer.add_scalar('others/explained_var', explained_var, global_step)
envs.close()
writer.close()
def main():
for env_id in ['Breakout']:
for seed in [1, 2, 3]:
print(env_id, seed)
train('ALE/' + env_id + '-v5', seed)
if __name__ == '__main__':
main()

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import argparse
import os
import time
import gymnasium as gym
import numpy as np
import torch
from stable_baselines3.common.atari_wrappers import FireResetEnv, EpisodicLifeEnv, ClipRewardEnv
from torch import nn, optim
from torch.distributions import Categorical
from torch.nn.utils.clip_grad import clip_grad_norm_
from torch.utils.tensorboard.writer import SummaryWriter
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py'))
parser.add_argument('--env_id', type=str, default='ALE/Breakout-v5')
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--use_cuda', type=bool, default=True)
parser.add_argument('--learning_rate', type=float, default=2.5e-4)
parser.add_argument('--lr_decay', type=bool, default=True)
parser.add_argument('--total_steps', type=int, default=int(1e7))
parser.add_argument('--num_envs', type=int, default=8)
parser.add_argument('--num_steps', type=int, default=128)
parser.add_argument('--update_epochs', type=int, default=8)
parser.add_argument('--num_mini_batches', type=int, default=4)
parser.add_argument('--gae_lambda', type=float, default=0.95)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--clip_value_loss', type=bool, default=True)
parser.add_argument('--c_1', type=float, default=1.0)
parser.add_argument('--c_2', type=float, default=0.01)
parser.add_argument('--clip_grad_norm', type=float, default=0.5)
parser.add_argument('--clip_epsilon', type=float, default=0.2)
args = parser.parse_args()
args.device = torch.device('cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu')
args.batch_size = int(args.num_envs * args.num_steps)
args.minibatch_size = int(args.batch_size // args.num_mini_batches)
args.num_updates = int(args.total_steps // args.batch_size)
return args
def make_env(env_id):
def thunk():
env = gym.make(env_id, frameskip=1, repeat_action_probability=0.0, full_action_space=False)
env = gym.wrappers.RecordEpisodeStatistics(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = EpisodicLifeEnv(env)
env = ClipRewardEnv(env)
env = gym.wrappers.AtariPreprocessing(env, scale_obs=True)
env = gym.wrappers.FrameStack(env, 4)
return env
return thunk
def compute_advantages(rewards, flags, values, last_value, args):
advantages = torch.zeros((args.num_steps, args.num_envs)).to(args.device)
adv = torch.zeros(args.num_envs).to(args.device)
for i in reversed(range(args.num_steps)):
returns = rewards[i] + args.gamma * flags[i] * last_value
delta = returns - values[i]
adv = delta + args.gamma * args.gae_lambda * flags[i] * adv
advantages[i] = adv
last_value = values[i]
return advantages
class Buffer:
def __init__(self, num_steps, num_envs, observation_shape, action_dim, device):
self.states = np.zeros((num_steps, num_envs, *observation_shape), dtype=np.float32)
self.actions = np.zeros((num_steps, num_envs), dtype=np.int64)
self.rewards = np.zeros((num_steps, num_envs), dtype=np.float32)
self.flags = np.zeros((num_steps, num_envs), dtype=np.float32)
self.log_probs = np.zeros((num_steps, num_envs), dtype=np.float32)
self.probs = np.zeros((num_steps, num_envs, action_dim), dtype=np.float32)
self.values = np.zeros((num_steps, num_envs), dtype=np.float32)
self.step = 0
self.num_steps = num_steps
self.device = device
def push(self, state, action, reward, flag, log_prob, prob, value):
self.states[self.step] = state
self.actions[self.step] = action
self.rewards[self.step] = reward
self.flags[self.step] = flag
self.log_probs[self.step] = log_prob
self.probs[self.step] = prob
self.values[self.step] = value
self.step = (self.step + 1) % self.num_steps
def get(self):
return (
torch.from_numpy(self.states).to(self.device),
torch.from_numpy(self.actions).to(self.device),
torch.from_numpy(self.rewards).to(self.device),
torch.from_numpy(self.flags).to(self.device),
torch.from_numpy(self.log_probs).to(self.device),
torch.from_numpy(self.values).to(self.device),
)
def get_probs(self):
return torch.from_numpy(self.probs).to(self.device)
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer
class Agent(nn.Module):
def __init__(self, action_dim, device):
super().__init__()
self.encoder = nn.Sequential(
layer_init(nn.Conv2d(4, 32, 8, stride=4)),
nn.ReLU(),
layer_init(nn.Conv2d(32, 64, 4, stride=2)),
nn.ReLU(),
layer_init(nn.Conv2d(64, 64, 3, stride=1)),
nn.ReLU(),
nn.Flatten(),
layer_init(nn.Linear(64 * 7 * 7, 512)),
nn.ReLU()
)
self.actor_net = layer_init(nn.Linear(512, action_dim), std=0.01)
self.critic_net = layer_init(nn.Linear(512, 1), std=1)
if device.type == 'cuda':
self.cuda()
def forward(self, state):
hidden = self.encoder(state)
actor_value = self.actor_net(hidden)
distribution = Categorical(logits=actor_value)
action = distribution.sample()
log_prob = distribution.log_prob(action)
value = self.critic_net(hidden).squeeze(-1)
return action, log_prob, value, distribution.probs
def evaluate(self, states, actions):
hidden = self.encoder(states)
actor_values = self.actor_net(hidden)
distribution = Categorical(logits=actor_values)
log_probs = distribution.log_prob(actions)
entropy = distribution.entropy()
values = self.critic_net(hidden).squeeze(-1)
return log_probs, values, entropy, distribution.probs
def critic(self, state):
return self.critic_net(self.encoder(state)).squeeze(-1)
def train(env_id, seed):
args = get_args()
args.env_id = env_id
args.seed = seed
run_name = (
'ppo_es' +
'_epoch_' + str(args.update_epochs) +
'_seed_' + str(args.seed)
)
# 保存训练日志
path_string = str(args.env_id)[4:] + '/' + run_name
writer = SummaryWriter(path_string)
writer.add_text(
'Hyperparameter',
'|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])),
)
# 初始化并行环境
envs = gym.vector.AsyncVectorEnv([make_env(args.env_id) for _ in range(args.num_envs)])
# 状态空间和动作空间
observation_shape = envs.single_observation_space.shape
action_dim = envs.single_action_space.n
# 随机数种子
if args.seed:
numpy_rng = np.random.default_rng(args.seed)
torch.manual_seed(args.seed)
state, _ = envs.reset(seed=args.seed)
else:
numpy_rng = np.random.default_rng()
state, _ = envs.reset()
# 价值网络和策略网络
agent = Agent(action_dim, args.device)
# 优化器
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate)
# 存储数据的buffer
rollout_buffer = Buffer(args.num_steps, args.num_envs, observation_shape, action_dim, args.device)
global_step = 0
start_time = time.time()
# 开始收集数据
for _ in tqdm(range(args.num_updates)):
# 学习率线性递减
if args.lr_decay:
optimizer.param_groups[0]['lr'] -= (args.learning_rate - 1e-12) / args.num_updates
for _ in range(args.num_steps):
global_step += 1 * args.num_envs
with torch.no_grad():
action, log_prob, value, prob = agent(torch.from_numpy(state).to(args.device).float())
action = action.cpu().numpy()
next_state, reward, terminated, truncated, all_info = envs.step(action)
# 存储数据
flag = 1.0 - np.logical_or(terminated, truncated)
log_prob = log_prob.cpu().numpy()
prob = prob.cpu().numpy()
value = value.cpu().numpy()
rollout_buffer.push(state, action, reward, flag, log_prob, prob, value)
state = next_state
if 'final_info' not in all_info:
continue
# 写入训练过程的数据
for info in all_info['final_info']:
if info is None:
continue
if 'episode' in info.keys():
writer.add_scalar('charts/episodic_return', info['episode']['r'], global_step)
# print(float(info['episode']['r']))
break
# ------------------------------- 上面收集了足够的数据，下面开始更新 ------------------------------- #
states, actions, rewards, flags, log_probs, values = rollout_buffer.get()
probs = rollout_buffer.get_probs()
with torch.no_grad():
last_value = agent.critic(torch.from_numpy(next_state).to(args.device).float())
# 计算优势值和TD目标
advantages = compute_advantages(rewards, flags, values, last_value, args)
td_target = advantages + values
# 将数据展平
states = states.reshape(-1, *observation_shape)
actions = actions.reshape(-1)
log_probs = log_probs.reshape(-1)
probs = probs.reshape((-1, action_dim))
td_target = td_target.reshape(-1)
advantages = advantages.reshape(-1)
values = values.reshape(-1)
batch_indexes = np.arange(args.batch_size)
# 更新策略网络和价值网络
flag = True
for e in range(1, args.update_epochs + 1):
numpy_rng.shuffle(batch_indexes)
for start in range(0, args.batch_size, args.minibatch_size):
end = start + args.minibatch_size
index = batch_indexes[start:end]
# 得到最新的策略网络和价值网络输出
new_log_probs, td_predict, entropy, new_probs = agent.evaluate(states[index], actions[index])
log_ratio = new_log_probs - log_probs[index]
ratios = log_ratio.exp()
# 计算kl散度
d = torch.sum(
probs[index] * torch.log((probs[index] + 1e-12) / (new_probs + 1e-12)), 1
)
writer.add_scalar('charts/average_kld', d.mean(), global_step)
writer.add_scalar('others/min_kld', d.min(), global_step)
writer.add_scalar('others/max_kld', d.max(), global_step)
# 早停策略
if d.mean() > 0.02:
flag = False
break
# 优势值标准化
b_advantages = advantages[index]
b_advantages = (b_advantages - b_advantages.mean()) / (b_advantages.std() + 1e-12)
# 策略网络损失
policy_loss_1 = b_advantages * ratios
policy_loss_2 = b_advantages * torch.clamp(
ratios, 1.0 - args.clip_epsilon, 1.0 + args.clip_epsilon
)
policy_loss = -torch.min(policy_loss_1, policy_loss_2).mean()
# 价值网络损失
if args.clip_value_loss:
v_loss_un_clipped = (td_predict - td_target[index]) ** 2
v_clipped = td_target[index] + torch.clamp(
td_predict - td_target[index],
-args.clip_epsilon,
args.clip_epsilon,
)
v_loss_clipped = (v_clipped - td_target[index]) ** 2
v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped)
value_loss = 0.5 * v_loss_max.mean()
else:
value_loss = 0.5 * ((td_predict - td_target[index]) ** 2).mean()
entropy_loss = entropy.mean()
# 保存训练过程中的一些数据
writer.add_scalar('losses/value_loss', value_loss.item(), global_step)
writer.add_scalar('losses/policy_loss', policy_loss.item(), global_step)
writer.add_scalar('losses/entropy', entropy_loss.item(), global_step)
writer.add_scalar('losses/delta', torch.abs(ratios - 1).mean().item(), global_step)
# 总的损失
loss = policy_loss + value_loss * args.c_1 - entropy_loss * args.c_2
# 更新网络参数
optimizer.zero_grad()
loss.backward()
clip_grad_norm_(agent.parameters(), args.clip_grad_norm)
optimizer.step()
if flag is False:
break
explained_var = (
np.nan if torch.var(td_target) == 0 else 1 - torch.var(td_target - values) / torch.var(td_target)
)
writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step)
writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step)
writer.add_scalar('others/explained_var', explained_var, global_step)
envs.close()
writer.close()
def main():
for env_id in ['Breakout']:
for seed in [1, 2, 3]:
print(env_id, seed)
train('ALE/' + env_id + '-v5', seed)
if __name__ == '__main__':
main()

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import argparse
import os
import time
import gymnasium as gym
import numpy as np
import torch
from stable_baselines3.common.atari_wrappers import FireResetEnv, EpisodicLifeEnv, ClipRewardEnv
from torch import nn, optim
from torch.distributions import Categorical
from torch.nn.utils.clip_grad import clip_grad_norm_
from torch.utils.tensorboard.writer import SummaryWriter
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py'))
parser.add_argument('--env_id', type=str, default='ALE/Breakout-v5')
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--use_cuda', type=bool, default=True)
parser.add_argument('--learning_rate', type=float, default=2.5e-4)
parser.add_argument('--lr_decay', type=bool, default=True)
parser.add_argument('--total_steps', type=int, default=int(1e7))
parser.add_argument('--num_envs', type=int, default=8)
parser.add_argument('--num_steps', type=int, default=128)
parser.add_argument('--update_epochs', type=int, default=8)
parser.add_argument('--num_mini_batches', type=int, default=4)
parser.add_argument('--gae_lambda', type=float, default=0.95)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--clip_value_loss', type=bool, default=True)
parser.add_argument('--c_1', type=float, default=1.0)
parser.add_argument('--c_2', type=float, default=0.01)
parser.add_argument('--clip_grad_norm', type=float, default=0.5)
parser.add_argument('--beta', type=float, default=1.0)
args = parser.parse_args()
args.device = torch.device('cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu')
args.batch_size = int(args.num_envs * args.num_steps)
args.minibatch_size = int(args.batch_size // args.num_mini_batches)
args.num_updates = int(args.total_steps // args.batch_size)
return args
def make_env(env_id):
def thunk():
env = gym.make(env_id, frameskip=1, repeat_action_probability=0.0, full_action_space=False)
env = gym.wrappers.RecordEpisodeStatistics(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = EpisodicLifeEnv(env)
env = ClipRewardEnv(env)
env = gym.wrappers.AtariPreprocessing(env, scale_obs=True)
env = gym.wrappers.FrameStack(env, 4)
return env
return thunk
def compute_advantages(rewards, flags, values, last_value, args):
advantages = torch.zeros((args.num_steps, args.num_envs)).to(args.device)
adv = torch.zeros(args.num_envs).to(args.device)
for i in reversed(range(args.num_steps)):
returns = rewards[i] + args.gamma * flags[i] * last_value
delta = returns - values[i]
adv = delta + args.gamma * args.gae_lambda * flags[i] * adv
advantages[i] = adv
last_value = values[i]
return advantages
class Buffer:
def __init__(self, num_steps, num_envs, observation_shape, action_dim, device):
self.states = np.zeros((num_steps, num_envs, *observation_shape), dtype=np.float32)
self.actions = np.zeros((num_steps, num_envs), dtype=np.int64)
self.rewards = np.zeros((num_steps, num_envs), dtype=np.float32)
self.flags = np.zeros((num_steps, num_envs), dtype=np.float32)
self.log_probs = np.zeros((num_steps, num_envs), dtype=np.float32)
self.probs = np.zeros((num_steps, num_envs, action_dim), dtype=np.float32)
self.values = np.zeros((num_steps, num_envs), dtype=np.float32)
self.step = 0
self.num_steps = num_steps
self.device = device
def push(self, state, action, reward, flag, log_prob, prob, value):
self.states[self.step] = state
self.actions[self.step] = action
self.rewards[self.step] = reward
self.flags[self.step] = flag
self.log_probs[self.step] = log_prob
self.probs[self.step] = prob
self.values[self.step] = value
self.step = (self.step + 1) % self.num_steps
def get(self):
return (
torch.from_numpy(self.states).to(self.device),
torch.from_numpy(self.actions).to(self.device),
torch.from_numpy(self.rewards).to(self.device),
torch.from_numpy(self.flags).to(self.device),
torch.from_numpy(self.log_probs).to(self.device),
torch.from_numpy(self.values).to(self.device),
)
def get_probs(self):
return torch.from_numpy(self.probs).to(self.device)
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer
class Agent(nn.Module):
def __init__(self, action_dim, device):
super().__init__()
self.encoder = nn.Sequential(
layer_init(nn.Conv2d(4, 32, 8, stride=4)),
nn.ReLU(),
layer_init(nn.Conv2d(32, 64, 4, stride=2)),
nn.ReLU(),
layer_init(nn.Conv2d(64, 64, 3, stride=1)),
nn.ReLU(),
nn.Flatten(),
layer_init(nn.Linear(64 * 7 * 7, 512)),
nn.ReLU()
)
self.actor_net = layer_init(nn.Linear(512, action_dim), std=0.01)
self.critic_net = layer_init(nn.Linear(512, 1), std=1)
if device.type == 'cuda':
self.cuda()
def forward(self, state):
hidden = self.encoder(state)
actor_value = self.actor_net(hidden)
distribution = Categorical(logits=actor_value)
action = distribution.sample()
log_prob = distribution.log_prob(action)
value = self.critic_net(hidden).squeeze(-1)
return action, log_prob, value, distribution.probs
def evaluate(self, states, actions):
hidden = self.encoder(states)
actor_values = self.actor_net(hidden)
distribution = Categorical(logits=actor_values)
log_probs = distribution.log_prob(actions)
entropy = distribution.entropy()
values = self.critic_net(hidden).squeeze(-1)
return log_probs, values, entropy, distribution.probs
def critic(self, state):
return self.critic_net(self.encoder(state)).squeeze(-1)
def train(env_id, seed):
args = get_args()
args.env_id = env_id
args.seed = seed
run_name = (
'ppo_penalty' +
'_epoch_' + str(args.update_epochs) +
'_seed_' + str(args.seed)
)
# 保存训练日志
path_string = str(args.env_id)[4:] + '/' + run_name
writer = SummaryWriter(path_string)
writer.add_text(
'Hyperparameter',
'|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])),
)
# 初始化并行环境
envs = gym.vector.AsyncVectorEnv([make_env(args.env_id) for _ in range(args.num_envs)])
# 状态空间和动作空间
observation_shape = envs.single_observation_space.shape
action_dim = envs.single_action_space.n
# 随机数种子
if args.seed:
numpy_rng = np.random.default_rng(args.seed)
torch.manual_seed(args.seed)
state, _ = envs.reset(seed=args.seed)
else:
numpy_rng = np.random.default_rng()
state, _ = envs.reset()
# 价值网络和策略网络
agent = Agent(action_dim, args.device)
# 优化器
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate)
# 存储数据的buffer
rollout_buffer = Buffer(args.num_steps, args.num_envs, observation_shape, action_dim, args.device)
global_step = 0
start_time = time.time()
# 开始收集数据
for _ in tqdm(range(args.num_updates)):
# 学习率线性递减
if args.lr_decay:
optimizer.param_groups[0]['lr'] -= (args.learning_rate - 1e-12) / args.num_updates
for _ in range(args.num_steps):
global_step += 1 * args.num_envs
with torch.no_grad():
action, log_prob, value, prob = agent(torch.from_numpy(state).to(args.device).float())
action = action.cpu().numpy()
next_state, reward, terminated, truncated, all_info = envs.step(action)
# 存储数据
flag = 1.0 - np.logical_or(terminated, truncated)
log_prob = log_prob.cpu().numpy()
prob = prob.cpu().numpy()
value = value.cpu().numpy()
rollout_buffer.push(state, action, reward, flag, log_prob, prob, value)
state = next_state
if 'final_info' not in all_info:
continue
# 写入训练过程的数据
for info in all_info['final_info']:
if info is None:
continue
if 'episode' in info.keys():
writer.add_scalar('charts/episodic_return', info['episode']['r'], global_step)
# print(float(info['episode']['r']))
break
# ------------------------------- 上面收集了足够的数据，下面开始更新 ------------------------------- #
states, actions, rewards, flags, log_probs, values = rollout_buffer.get()
probs = rollout_buffer.get_probs()
with torch.no_grad():
last_value = agent.critic(torch.from_numpy(next_state).to(args.device).float())
# 计算优势值和TD目标
advantages = compute_advantages(rewards, flags, values, last_value, args)
td_target = advantages + values
# 将数据展平
states = states.reshape(-1, *observation_shape)
actions = actions.reshape(-1)
log_probs = log_probs.reshape(-1)
probs = probs.reshape((-1, action_dim))
td_target = td_target.reshape(-1)
advantages = advantages.reshape(-1)
values = values.reshape(-1)
batch_indexes = np.arange(args.batch_size)
# 更新策略网络和价值网络
for e in range(1, args.update_epochs + 1):
numpy_rng.shuffle(batch_indexes)
for start in range(0, args.batch_size, args.minibatch_size):
end = start + args.minibatch_size
index = batch_indexes[start:end]
# 得到最新的策略网络和价值网络输出
new_log_probs, td_predict, entropy, new_probs = agent.evaluate(states[index], actions[index])
log_ratio = new_log_probs - log_probs[index]
ratios = log_ratio.exp()
# 计算kl散度
d = torch.sum(
probs[index] * torch.log((probs[index] + 1e-12) / (new_probs + 1e-12)), 1
)
writer.add_scalar('charts/average_kld', d.mean(), global_step)
writer.add_scalar('others/min_kld', d.min(), global_step)
writer.add_scalar('others/max_kld', d.max(), global_step)
# 优势值标准化
b_advantages = advantages[index]
b_advantages = (b_advantages - b_advantages.mean()) / (b_advantages.std() + 1e-12)
# 策略网络损失
if d.mean() < 0.02 / 1.5:
args.beta = args.beta / 2
if d.mean() > 0.02 * 1.5:
args.beta = args.beta * 2
policy_loss = -(b_advantages * ratios - args.beta * d).mean()
# 价值网络损失
if args.clip_value_loss:
v_loss_un_clipped = (td_predict - td_target[index]) ** 2
v_clipped = td_target[index] + torch.clamp(
td_predict - td_target[index],
-0.2,
0.2,
)
v_loss_clipped = (v_clipped - td_target[index]) ** 2
v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped)
value_loss = 0.5 * v_loss_max.mean()
else:
value_loss = 0.5 * ((td_predict - td_target[index]) ** 2).mean()
entropy_loss = entropy.mean()
# 保存训练过程中的一些数据
writer.add_scalar('losses/value_loss', value_loss.item(), global_step)
writer.add_scalar('losses/policy_loss', policy_loss.item(), global_step)
writer.add_scalar('losses/entropy', entropy_loss.item(), global_step)
writer.add_scalar('losses/delta', torch.abs(ratios - 1).mean().item(), global_step)
# 总的损失
loss = policy_loss + value_loss * args.c_1 - entropy_loss * args.c_2
# 更新网络参数
optimizer.zero_grad()
loss.backward()
clip_grad_norm_(agent.parameters(), args.clip_grad_norm)
optimizer.step()
explained_var = (
np.nan if torch.var(td_target) == 0 else 1 - torch.var(td_target - values) / torch.var(td_target)
)
writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step)
writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step)
writer.add_scalar('others/explained_var', explained_var, global_step)
envs.close()
writer.close()
def main():
for env_id in ['Breakout']:
for seed in [1, 2, 3]:
print(env_id, seed)
train('ALE/' + env_id + '-v5', seed)
if __name__ == '__main__':
main()

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import argparse
import os
import time
import gymnasium as gym
import numpy as np
import torch
from stable_baselines3.common.atari_wrappers import FireResetEnv, EpisodicLifeEnv, ClipRewardEnv
from torch import nn, optim
from torch.distributions import Categorical
from torch.nn.utils.clip_grad import clip_grad_norm_
from torch.utils.tensorboard.writer import SummaryWriter
from tqdm import tqdm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name', type=str, default=os.path.basename(__file__).rstrip('.py'))
parser.add_argument('--env_id', type=str, default='ALE/Breakout-v5')
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--use_cuda', type=bool, default=True)
parser.add_argument('--learning_rate', type=float, default=2.5e-4)
parser.add_argument('--lr_decay', type=bool, default=True)
parser.add_argument('--total_steps', type=int, default=int(1e7))
parser.add_argument('--num_envs', type=int, default=8)
parser.add_argument('--num_steps', type=int, default=128)
parser.add_argument('--update_epochs', type=int, default=8)
parser.add_argument('--num_mini_batches', type=int, default=4)
parser.add_argument('--gae_lambda', type=float, default=0.95)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--clip_value_loss', type=bool, default=True)
parser.add_argument('--c_1', type=float, default=1.0)
parser.add_argument('--c_2', type=float, default=0.01)
parser.add_argument('--clip_grad_norm', type=float, default=0.5)
parser.add_argument('--kld_max', type=float, default=0.01)
args = parser.parse_args()
args.device = torch.device('cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu')
args.batch_size = int(args.num_envs * args.num_steps)
args.minibatch_size = int(args.batch_size // args.num_mini_batches)
args.num_updates = int(args.total_steps // args.batch_size)
return args
def make_env(env_id):
def thunk():
env = gym.make(env_id, frameskip=1, repeat_action_probability=0.0, full_action_space=False)
env = gym.wrappers.RecordEpisodeStatistics(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = EpisodicLifeEnv(env)
env = ClipRewardEnv(env)
env = gym.wrappers.AtariPreprocessing(env, scale_obs=True)
env = gym.wrappers.FrameStack(env, 4)
return env
return thunk
def compute_advantages(rewards, flags, values, last_value, args):
advantages = torch.zeros((args.num_steps, args.num_envs)).to(args.device)
adv = torch.zeros(args.num_envs).to(args.device)
for i in reversed(range(args.num_steps)):
returns = rewards[i] + args.gamma * flags[i] * last_value
delta = returns - values[i]
adv = delta + args.gamma * args.gae_lambda * flags[i] * adv
advantages[i] = adv
last_value = values[i]
return advantages
class Buffer:
def __init__(self, num_steps, num_envs, observation_shape, action_dim, device):
self.states = np.zeros((num_steps, num_envs, *observation_shape), dtype=np.float32)
self.actions = np.zeros((num_steps, num_envs), dtype=np.int64)
self.rewards = np.zeros((num_steps, num_envs), dtype=np.float32)
self.flags = np.zeros((num_steps, num_envs), dtype=np.float32)
self.log_probs = np.zeros((num_steps, num_envs), dtype=np.float32)
self.probs = np.zeros((num_steps, num_envs, action_dim), dtype=np.float32)
self.values = np.zeros((num_steps, num_envs), dtype=np.float32)
self.step = 0
self.num_steps = num_steps
self.device = device
def push(self, state, action, reward, flag, log_prob, prob, value):
self.states[self.step] = state
self.actions[self.step] = action
self.rewards[self.step] = reward
self.flags[self.step] = flag
self.log_probs[self.step] = log_prob
self.probs[self.step] = prob
self.values[self.step] = value
self.step = (self.step + 1) % self.num_steps
def get(self):
return (
torch.from_numpy(self.states).to(self.device),
torch.from_numpy(self.actions).to(self.device),
torch.from_numpy(self.rewards).to(self.device),
torch.from_numpy(self.flags).to(self.device),
torch.from_numpy(self.log_probs).to(self.device),
torch.from_numpy(self.values).to(self.device),
)
def get_probs(self):
return torch.from_numpy(self.probs).to(self.device)
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer
class Agent(nn.Module):
def __init__(self, action_dim, device):
super().__init__()
self.encoder = nn.Sequential(
layer_init(nn.Conv2d(4, 32, 8, stride=4)),
nn.ReLU(),
layer_init(nn.Conv2d(32, 64, 4, stride=2)),
nn.ReLU(),
layer_init(nn.Conv2d(64, 64, 3, stride=1)),
nn.ReLU(),
nn.Flatten(),
layer_init(nn.Linear(64 * 7 * 7, 512)),
nn.ReLU()
)
self.actor_net = layer_init(nn.Linear(512, action_dim), std=0.01)
self.critic_net = layer_init(nn.Linear(512, 1), std=1)
if device.type == 'cuda':
self.cuda()
def forward(self, state):
hidden = self.encoder(state)
actor_value = self.actor_net(hidden)
distribution = Categorical(logits=actor_value)
action = distribution.sample()
log_prob = distribution.log_prob(action)
value = self.critic_net(hidden).squeeze(-1)
return action, log_prob, value, distribution.probs
def evaluate(self, states, actions):
hidden = self.encoder(states)
actor_values = self.actor_net(hidden)
distribution = Categorical(logits=actor_values)
log_probs = distribution.log_prob(actions)
entropy = distribution.entropy()
values = self.critic_net(hidden).squeeze(-1)
return log_probs, values, entropy, distribution.probs
def critic(self, state):
return self.critic_net(self.encoder(state)).squeeze(-1)
def train(env_id, seed):
args = get_args()
args.env_id = env_id
args.seed = seed
run_name = (
'spo_' + str(args.kld_max) +
'_epoch_' + str(args.update_epochs) +
'_seed_' + str(args.seed)
)
# 保存训练日志
path_string = str(args.env_id)[4:] + '/' + run_name
writer = SummaryWriter(path_string)
writer.add_text(
'Hyperparameter',
'|param|value|\n|-|-|\n%s' % ('\n'.join([f'|{key}|{value}|' for key, value in vars(args).items()])),
)
# 初始化并行环境
envs = gym.vector.AsyncVectorEnv([make_env(args.env_id) for _ in range(args.num_envs)])
# 状态空间和动作空间
observation_shape = envs.single_observation_space.shape
action_dim = envs.single_action_space.n
# 随机数种子
if args.seed:
numpy_rng = np.random.default_rng(args.seed)
torch.manual_seed(args.seed)
state, _ = envs.reset(seed=args.seed)
else:
numpy_rng = np.random.default_rng()
state, _ = envs.reset()
# 价值网络和策略网络
agent = Agent(action_dim, args.device)
# 优化器
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate)
# 存储数据的buffer
rollout_buffer = Buffer(args.num_steps, args.num_envs, observation_shape, action_dim, args.device)
global_step = 0
start_time = time.time()
# 开始收集数据
for _ in tqdm(range(args.num_updates)):
# 学习率线性递减
if args.lr_decay:
optimizer.param_groups[0]['lr'] -= (args.learning_rate - 1e-12) / args.num_updates
for _ in range(args.num_steps):
global_step += 1 * args.num_envs
with torch.no_grad():
action, log_prob, value, prob = agent(torch.from_numpy(state).to(args.device).float())
action = action.cpu().numpy()
next_state, reward, terminated, truncated, all_info = envs.step(action)
# 存储数据
flag = 1.0 - np.logical_or(terminated, truncated)
log_prob = log_prob.cpu().numpy()
prob = prob.cpu().numpy()
value = value.cpu().numpy()
rollout_buffer.push(state, action, reward, flag, log_prob, prob, value)
state = next_state
if 'final_info' not in all_info:
continue
# 写入训练过程的数据
for info in all_info['final_info']:
if info is None:
continue
if 'episode' in info.keys():
writer.add_scalar('charts/episodic_return', info['episode']['r'], global_step)
# print(float(info['episode']['r']))
break
# ------------------------------- 上面收集了足够的数据，下面开始更新 ------------------------------- #
states, actions, rewards, flags, log_probs, values = rollout_buffer.get()
probs = rollout_buffer.get_probs()
with torch.no_grad():
last_value = agent.critic(torch.from_numpy(next_state).to(args.device).float())
# 计算优势值和TD目标
advantages = compute_advantages(rewards, flags, values, last_value, args)
td_target = advantages + values
# 将数据展平
states = states.reshape(-1, *observation_shape)
actions = actions.reshape(-1)
log_probs = log_probs.reshape(-1)
probs = probs.reshape((-1, action_dim))
td_target = td_target.reshape(-1)
advantages = advantages.reshape(-1)
values = values.reshape(-1)
batch_indexes = np.arange(args.batch_size)
# 更新策略网络和价值网络
for e in range(1, args.update_epochs + 1):
numpy_rng.shuffle(batch_indexes)
t = 0
for start in range(0, args.batch_size, args.minibatch_size):
t += 1
end = start + args.minibatch_size
index = batch_indexes[start:end]
# 得到最新的策略网络和价值网络输出
new_log_probs, td_predict, entropy, new_probs = agent.evaluate(states[index], actions[index])
log_ratio = new_log_probs - log_probs[index]
ratios = log_ratio.exp()
# 计算kl散度
d = torch.sum(
probs[index] * torch.log((probs[index] + 1e-12) / (new_probs + 1e-12)), 1
)
writer.add_scalar('charts/average_kld', d.mean(), global_step)
writer.add_scalar('others/min_kld', d.min(), global_step)
writer.add_scalar('others/max_kld', d.max(), global_step)
# 优势值标准化
b_advantages = advantages[index]
b_advantages = (b_advantages - b_advantages.mean()) / (b_advantages.std() + 1e-12)
# 策略网络和价值网络损失
if e == 1 and t == 1:
policy_loss = (-b_advantages * ratios).mean()
else:
# d_clip
d_clip = torch.clamp(input=d, min=0, max=args.kld_max)
# d_clip / d
ratio = d_clip / (d + 1e-12)
# sign_a
sign_a = torch.sign(b_advantages)
# (d_clip / d + sign_a - 1) * sign_a
result = (ratio + sign_a - 1) * sign_a
# 策略网络损失
policy_loss = (-b_advantages * ratios * result).mean()
# 价值网络损失
if args.clip_value_loss:
v_loss_un_clipped = (td_predict - td_target[index]) ** 2
v_clipped = td_target[index] + torch.clamp(
td_predict - td_target[index],
-0.2,
0.2,
)
v_loss_clipped = (v_clipped - td_target[index]) ** 2
v_loss_max = torch.max(v_loss_un_clipped, v_loss_clipped)
value_loss = 0.5 * v_loss_max.mean()
else:
value_loss = 0.5 * ((td_predict - td_target[index]) ** 2).mean()
entropy_loss = entropy.mean()
# 保存训练过程中的一些数据
writer.add_scalar('losses/value_loss', value_loss.item(), global_step)
writer.add_scalar('losses/policy_loss', policy_loss.item(), global_step)
writer.add_scalar('losses/entropy', entropy_loss.item(), global_step)
writer.add_scalar('losses/delta', torch.abs(ratios - 1).mean().item(), global_step)
# 总的损失
loss = policy_loss + value_loss * args.c_1 - entropy_loss * args.c_2
# 更新网络参数
optimizer.zero_grad()
loss.backward()
clip_grad_norm_(agent.parameters(), args.clip_grad_norm)
optimizer.step()
explained_var = (
np.nan if torch.var(td_target) == 0 else 1 - torch.var(td_target - values) / torch.var(td_target)
)
writer.add_scalar('charts/learning_rate', optimizer.param_groups[0]['lr'], global_step)
writer.add_scalar('charts/SPS', int(global_step / (time.time() - start_time)), global_step)
writer.add_scalar('others/explained_var', explained_var, global_step)
envs.close()
writer.close()
def main():
for env_id in ['Breakout']:
for seed in [1, 2, 3]:
print(env_id, seed)
train('ALE/' + env_id + '-v5', seed)
if __name__ == '__main__':
main()