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v4/ppo.py
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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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v4/spo.py
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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()