强化学习经典算法笔记(十二)：近端策略优化算法（PPO）实现，基于A2C（下）
强化学习经典算法笔记(十二)：近端策略优化算法（PPO）实现，基于A2C
本篇实现一个基于A2C框架的PPO算法，应用于连续动作空间任务。
import torch
import torch.nn as nn
from torch.distributions import MultivariateNormal
import gym
import numpy as np
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class Memory:
def __init__(self):
self.actions = []
self.states = []
self.logprobs = []
self.rewards = []
self.is_terminals = []
def clear_memory(self):
del self.actions[:]
del self.states[:]
del self.logprobs[:]
del self.rewards[:]
del self.is_terminals[:]
A2C的实现和上篇的区别在于动作的选择。Actor输出多变量高斯分布的均值向量，人为给定一个协方差矩阵，当然Var也可以学习出来。
class ActorCritic(nn.Module):
def __init__(self, state_dim, action_dim, action_std):
super(ActorCritic, self).__init__()
# action mean range -1 to 1
self.actor = nn.Sequential(
nn.Linear(state_dim, 64),
nn.Tanh(),
nn.Linear(64, 32),
nn.Tanh(),
nn.Linear(32, action_dim),
nn.Tanh()
)
# critic
self.critic = nn.Sequential(
nn.Linear(state_dim, 64),
nn.Tanh(),
nn.Linear(64, 32),
nn.Tanh(),
nn.Linear(32, 1)
)
self.action_var = torch.full((action_dim,), action_std*action_std).to(device)
def forward(self):
raise NotImplementedError
def act(self, state, memory):
action_mean = self.actor(state)
cov_mat = torch.diag(self.action_var).to(device)
dist = MultivariateNormal(action_mean, cov_mat)
action = dist.sample()
action_logprob = dist.log_prob(action)
memory.states.append(state)
memory.actions.append(action)
memory.logprobs.append(action_logprob)
return action.detach()
def evaluate(self, state, action):
action_mean = torch.squeeze(self.actor(state))
action_var = self.action_var.expand_as(action_mean)
cov_mat = torch.diag_embed(action_var).to(device)
dist = MultivariateNormal(action_mean, cov_mat)
action_logprobs = dist.log_prob(torch.squeeze(action))
dist_entropy = dist.entropy()
state_value = self.critic(state)
return action_logprobs, torch.squeeze(state_value), dist_entropy
class PPO:
def __init__(self, state_dim, action_dim, action_std, lr, betas, gamma, K_epochs, eps_clip):
self.lr = lr
self.betas = betas
self.gamma = gamma
self.eps_clip = eps_clip
self.K_epochs = K_epochs
self.policy = ActorCritic(state_dim, action_dim, action_std).to(device)
self.optimizer = torch.optim.Adam(self.policy.parameters(), lr=lr, betas=betas)
self.policy_old = ActorCritic(state_dim, action_dim, action_std).to(device)
self.policy_old.load_state_dict(self.policy.state_dict())
self.MseLoss = nn.MSELoss()
def select_action(self, state, memory):
state = torch.FloatTensor(state.reshape(1, -1)).to(device)
return self.policy_old.act(state, memory).cpu().data.numpy().flatten()
def update(self, memory):
# Monte Carlo estimate of rewards:
rewards = []
discounted_reward = 0
for reward, is_terminal in zip(reversed(memory.rewards), reversed(memory.is_terminals)):
if is_terminal:
discounted_reward = 0
discounted_reward = reward + (self.gamma * discounted_reward)
rewards.insert(0, discounted_reward)
# Normalizing the rewards:
rewards = torch.tensor(rewards).to(device)
rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-5)
# convert list to tensor
old_states = torch.squeeze(torch.stack(memory.states).to(device)).detach()
old_actions = torch.squeeze(torch.stack(memory.actions).to(device)).detach()
old_logprobs = torch.squeeze(torch.stack(memory.logprobs)).to(device).detach()
# Optimize policy for K epochs:
for _ in range(self.K_epochs):
# Evaluating old actions and values :
logprobs, state_values, dist_entropy = self.policy.evaluate(old_states, old_actions)
# Finding the ratio (pi_theta / pi_theta__old):
ratios = torch.exp(logprobs - old_logprobs.detach())
# Finding Surrogate Loss:
advantages = rewards - state_values.detach()
surr1 = ratios * advantages
surr2 = torch.clamp(ratios, 1-self.eps_clip, 1+self.eps_clip) * advantages
loss = -torch.min(surr1, surr2) + 0.5*self.MseLoss(state_values, rewards) - 0.01*dist_entropy
# take gradient step
self.optimizer.zero_grad()
loss.mean().backward()
self.optimizer.step()
# Copy new weights into old policy:
self.policy_old.load_state_dict(self.policy.state_dict())
def main():
############## Hyperparameters ##############
env_name = "BipedalWalker-v2"
render = False
solved_reward = 300 # stop training if avg_reward > solved_reward
log_interval = 20 # print avg reward in the interval
max_episodes = 10000 # max training episodes
max_timesteps = 1500 # max timesteps in one episode
update_timestep = 4000 # update policy every n timesteps
action_std = 0.5 # constant std for action distribution (Multivariate Normal)
K_epochs = 80 # update policy for K epochs
eps_clip = 0.2 # clip parameter for PPO
gamma = 0.99 # discount factor
lr = 0.0003 # parameters for Adam optimizer
betas = (0.9, 0.999)
random_seed = None
#############################################
# creating environment
env = gym.make(env_name)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
if random_seed:
print("Random Seed: {}".format(random_seed))
torch.manual_seed(random_seed)
env.seed(random_seed)
np.random.seed(random_seed)
memory = Memory()
ppo = PPO(state_dim, action_dim, action_std, lr, betas, gamma, K_epochs, eps_clip)
print(lr,betas)
# logging variables
running_reward = 0
avg_length = 0
time_step = 0
# training loop
for i_episode in range(1, max_episodes+1):
state = env.reset()
for t in range(max_timesteps):
time_step +=1
# Running policy_old:
action = ppo.select_action(state, memory)
state, reward, done, _ = env.step(action)
# Saving reward and is_terminals:
memory.rewards.append(reward)
memory.is_terminals.append(done)
# update if its time
if time_step % update_timestep == 0:
ppo.update(memory)
memory.clear_memory()
time_step = 0
running_reward += reward
if render:
env.render()
if done:
break
avg_length += t
# stop training if avg_reward > solved_reward
if running_reward > (log_interval*solved_reward):
print("########## Solved! ##########")
torch.save(ppo.policy.state_dict(), './PPO_continuous_solved_{}.pth'.format(env_name))
break
# save every 500 episodes
if i_episode % 500 == 0:
torch.save(ppo.policy.state_dict(), './PPO_continuous_{}.pth'.format(env_name))
# logging
if i_episode % log_interval == 0:
avg_length = int(avg_length/log_interval)
running_reward = int((running_reward/log_interval))
print('Episode {} \t Avg length: {} \t Avg reward: {}'.format(i_episode, avg_length, running_reward))
running_reward = 0
avg_length = 0
if __name__ == '__main__':
main()
测试代码
import gym
from PPO_continuous import PPO, Memory
from PIL import Image
import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def test():
############## Hyperparameters ##############
env_name = "BipedalWalker-v2"
env = gym.make(env_name)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
n_episodes = 3 # num of episodes to run
max_timesteps = 1500 # max timesteps in one episode
render = True # render the environment
save_gif = False # png images are saved in gif folder
# filename and directory to load model from
filename = "PPO_continuous_" +env_name+ ".pth"
directory = "./preTrained/"
action_std = 0.5 # constant std for action distribution (Multivariate Normal)
K_epochs = 80 # update policy for K epochs
eps_clip = 0.2 # clip parameter for PPO
gamma = 0.99 # discount factor
lr = 0.0003 # parameters for Adam optimizer
betas = (0.9, 0.999)
#############################################
memory = Memory()
ppo = PPO(state_dim, action_dim, action_std, lr, betas, gamma, K_epochs, eps_clip)
ppo.policy_old.load_state_dict(torch.load(directory+filename))
for ep in range(1, n_episodes+1):
ep_reward = 0
state = env.reset()
for t in range(max_timesteps):
action = ppo.select_action(state, memory)
state, reward, done, _ = env.step(action)
ep_reward += reward
if render:
env.render()
if save_gif:
img = env.render(mode = 'rgb_array')
img = Image.fromarray(img)
img.save('./gif/{}.jpg'.format(t))
if done:
break
print('Episode: {}\tReward: {}'.format(ep, int(ep_reward)))
ep_reward = 0
env.close()
if __name__ == '__main__':
test()