本篇文章是 OpenAI Spinnging Up 中 Part 3: Intro to Policy Optimization 中代码的学习笔记, 原文在 https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html , 代码在 https://github.com/openai/spinningup/blob/master/spinup/examples/pytorch/pg_math/1_simple_pg.py .

先给出代码

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import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
from torch.optim import Adam
import numpy as np
import gym
from gym.spaces import Discrete, Box

def mlp(sizes, activation=nn.Tanh, output_activation=nn.Identity):
# Build a feedforward neural network.
layers = []
for j in range(len(sizes)-1):
act = activation if j < len(sizes)-2 else output_activation
layers += [nn.Linear(sizes[j], sizes[j+1]), act()]
return nn.Sequential(*layers)

def train(env_name='CartPole-v0', hidden_sizes=[32], lr=1e-2,
epochs=50, batch_size=5000, render=False):

# make environment, check spaces, get obs / act dims
env = gym.make(env_name)
assert isinstance(env.observation_space, Box), \
"This example only works for envs with continuous state spaces."
assert isinstance(env.action_space, Discrete), \
"This example only works for envs with discrete action spaces."

obs_dim = env.observation_space.shape[0]
n_acts = env.action_space.n

# make core of policy network
logits_net = mlp(sizes=[obs_dim]+hidden_sizes+[n_acts])

# make function to compute action distribution
def get_policy(obs):
logits = logits_net(obs)
return Categorical(logits=logits)

# make action selection function (outputs int actions, sampled from policy)
def get_action(obs):
return get_policy(obs).sample().item()

# make loss function whose gradient, for the right data, is policy gradient
def compute_loss(obs, act, weights):
logp = get_policy(obs).log_prob(act)
return -(logp * weights).mean()

# make optimizer
optimizer = Adam(logits_net.parameters(), lr=lr)

# for training policy
def train_one_epoch():
# make some empty lists for logging.
batch_obs = [] # for observations
batch_acts = [] # for actions
batch_weights = [] # for R(tau) weighting in policy gradient
batch_rets = [] # for measuring episode returns
batch_lens = [] # for measuring episode lengths

# reset episode-specific variables
obs = env.reset() # first obs comes from starting distribution
done = False # signal from environment that episode is over
ep_rews = [] # list for rewards accrued throughout ep

# render first episode of each epoch
finished_rendering_this_epoch = False

# collect experience by acting in the environment with current policy
while True:

# rendering
if (not finished_rendering_this_epoch) and render:
env.render()

# save obs
batch_obs.append(obs.copy())

# act in the environment
act = get_action(torch.as_tensor(obs, dtype=torch.float32))
obs, rew, done, _ = env.step(act)

# save action, reward
batch_acts.append(act)
ep_rews.append(rew)

if done:
# if episode is over, record info about episode
ep_ret, ep_len = sum(ep_rews), len(ep_rews)
batch_rets.append(ep_ret)
batch_lens.append(ep_len)

# the weight for each logprob(a|s) is R(tau)
batch_weights += [ep_ret] * ep_len

# reset episode-specific variables
obs, done, ep_rews = env.reset(), False, []

# won't render again this epoch
finished_rendering_this_epoch = True

# end experience loop if we have enough of it
if len(batch_obs) > batch_size:
break

# take a single policy gradient update step
optimizer.zero_grad()
batch_loss = compute_loss(obs=torch.as_tensor(batch_obs, dtype=torch.float32),
act=torch.as_tensor(batch_acts, dtype=torch.int32),
weights=torch.as_tensor(batch_weights, dtype=torch.float32)
)
batch_loss.backward()
optimizer.step()
return batch_loss, batch_rets, batch_lens

# training loop
for i in range(epochs):
batch_loss, batch_rets, batch_lens = train_one_epoch()
print('epoch: %3d \t loss: %.3f \t return: %.3f \t ep_len: %.3f'%
(i, batch_loss, np.mean(batch_rets), np.mean(batch_lens)))

if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--env_name', '--env', type=str, default='CartPole-v0')
parser.add_argument('--render', action='store_true')
parser.add_argument('--lr', type=float, default=1e-2)
args, unknown = parser.parse_known_args()
print('\nUsing simplest formulation of policy gradient.\n')
train(env_name=args.env_name, render=args.render, lr=args.lr)

这里我们会对大部分函数以及一些变量一一解析, 其中一些 Pytorch 的 API 可以参考我的这篇文章或者官方文档 .

mlp

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def mlp(sizes, activation=nn.Tanh, output_activation=nn.Identity):
# Build a feedforward neural network.
layers = []
for j in range(len(sizes)-1):
act = activation if j < len(sizes)-2 else output_activation
layers += [nn.Linear(sizes[j], sizes[j+1]), act()]
return nn.Sequential(*layers)

依据输入返回一个神经网络.

参数

  • sizes

    其中包含神经网络的层数以及节点数信息

  • activation

    节点的激活函数, 这里默认是 nn.Tanh 也就是函数

  • output_activation

    输出的激活函数

解析

layers 中的每一个元素就是神经网络的一部分 (节点与激活函数), 而 nn.Sequential(*layers) 是将这些部分组合成一个神经网络. 其中

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for j in range(len(sizes)-1):
act = activation if j < len(sizes)-2 else output_activation
layers += [nn.Linear(sizes[j], sizes[j+1]), act()]

这个循环, act 指的是激活函数, 当该层不是最后一层时使用 activation , 是时使用 output_activation 作为激活函数.

get_policy

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def get_policy(obs):
logits = logits_net(obs)
return Categorical(logits=logits)

依据环境计算出动作的对数概率, 并依此返回一个 Categorical 对象.

参数

  • obs

    环境的参数, 描述了环境

解析

logits_net 是一个神经网络, 接受参数后输出最终结果 (动作的对数概率). 至于 Categorical 对象请自行了解.

get_action

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def get_action(obs):
return get_policy(obs).sample().item()

参数

  • obs

    环境的参数, 描述了环境.

解析

利用 Categorical 对象采样动作.

compute_loss

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def compute_loss(obs, act, weights):
logp = get_policy(obs).log_prob(act)
return -(logp * weights).mean()

计算损失.

参数

  • obs

    环境的参数, 描述了环境

  • act

    采样的动作

  • weights

    某项的权重

解析

损失函数对参数的梯度要和期望回报对参数的梯度相同, 而期望回报对参数的梯度的估计式为

logp 其实就是, 而 weight 其实就是. 因此该函数返回的其实就是

求导后正是我们的梯度.

train_one_epoch

这是训练一个 epoch 的函数 (神经网络参数更新一次) .

解析

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if done:
# if episode is over, record info about episode
ep_ret, ep_len = sum(ep_rews), len(ep_rews)
batch_rets.append(ep_ret)
batch_lens.append(ep_len)

# the weight for each logprob(a|s) is R(tau)
batch_weights += [ep_ret] * ep_len

# reset episode-specific variables
obs, done, ep_rews = env.reset(), False, []

# won't render again this epoch
finished_rendering_this_epoch = True

# end experience loop if we have enough of it
if len(batch_obs) > batch_size:
break
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# take a single policy gradient update step
optimizer.zero_grad()
batch_loss = compute_loss(obs=torch.as_tensor(batch_obs, dtype=torch.float32),
act=torch.as_tensor(batch_acts, dtype=torch.int32),
weights=torch.as_tensor(batch_weights, dtype=torch.float32)
)
batch_loss.backward()
optimizer.step()

依据 batch_size 确定走一个 epoch 走多少步. 然后当某个轨迹结束时 (也就是 done ) , 会计算总的回报, 然后通过 compute_loss 计算损失, 同时通过 Pytorch 的自动求导机制算出梯度, 然后用 optimizer (Adam 算法) 更新.

train

整个过程其实就是重复多个 epoch , 然后最终训练 epochs 次. 如果需要利用 Gym 的可视化, 可以将 render 参数设为 True

参数

  • env_name

    Gym 环境的名称

  • hidden_sizes

    神经网络隐藏节点数, 可以自行调整

  • lr

    学习率.

  • epochs

    训练的 epoch 数

  • batch_size

    一次 epoch 行动的次数

  • render

    Gym 是否可视化 (True or False)