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PPO.py
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import torch
import torch.nn as nn
from torch.distributions import MultivariateNormal
from torch.distributions import Categorical
################################## set device ##################################
#print("============================================================================================")
# set device to cpu or cuda
device = torch.device('cpu')
if(torch.cuda.is_available()):
device = torch.device('cuda:0')
torch.cuda.empty_cache()
#print("Device set to : " + str(torch.cuda.get_device_name(device)))
else:
pass
#print("Device set to : cpu")
#print("============================================================================================")
################################## PPO Policy ##################################
class RolloutBuffer:
def __init__(self):
self.actions = []
self.states = []
self.observations = []
self.logprobs = []
self.rewards = []
self.is_terminals = []
def argsort(self,seq):
return sorted(range(len(seq)), key=seq.__getitem__, reverse=True)
def clear(self):
del self.actions[:]
del self.states[:]
del self.observations[:]
del self.logprobs[:]
del self.rewards[:]
del self.is_terminals[:]
class ActorCritic(nn.Module):
def __init__(self, nagents, state_dim, action_dim, has_continuous_action_space, action_std_init, device=device):
super(ActorCritic, self).__init__()
self.has_continuous_action_space = has_continuous_action_space
if has_continuous_action_space:
self.action_dim = action_dim
self.action_var = torch.full((action_dim,), action_std_init * action_std_init).to(device)
size = 256
# actor
if has_continuous_action_space :
self.actor = nn.Sequential(
nn.Linear(state_dim, size),
nn.Tanh(),
nn.Linear(size, 2*size),
nn.Tanh(),
nn.Linear(2*size, size),
nn.Tanh(),
nn.Linear(size, action_dim),
)
else:
self.actor = nn.Sequential(
nn.Linear(state_dim, size),
nn.Tanh(),
nn.Linear(size, 2*size),
nn.Tanh(),
nn.Linear(2*size, size),
nn.Tanh(),
nn.Linear(size, action_dim),
nn.Softmax(dim=-1)
)
# critic
self.critic = nn.Sequential(
nn.Linear(state_dim*nagents, size),
nn.Tanh(),
nn.Linear(size, 2*size),
nn.Tanh(),
nn.Linear(2*size, size),
nn.Tanh(),
nn.Linear(size, 1)
)
def set_action_std(self, new_action_std):
if self.has_continuous_action_space:
self.action_var = torch.full((self.action_dim,), new_action_std * new_action_std).to(device)
else:
print("--------------------------------------------------------------------------------------------")
print("WARNING : Calling ActorCritic::set_action_std() on discrete action space policy")
print("--------------------------------------------------------------------------------------------")
def forward(self):
raise NotImplementedError
def act(self, state):
if self.has_continuous_action_space:
action_mean = self.actor(state)
cov_mat = torch.diag(self.action_var).unsqueeze(dim=0)
dist = MultivariateNormal(action_mean, cov_mat)
else:
action_probs = self.actor(state)
dist = Categorical(action_probs)
action = dist.sample()
action_logprob = dist.log_prob(action)
return action.detach(), action_logprob.detach()
def act2(self, state):
if self.has_continuous_action_space:
action_mean = self.actor(state)
cov_mat = torch.diag(self.action_var).unsqueeze(dim=0)
dist = MultivariateNormal(action_mean, cov_mat)
else:
action_probs = self.actor(state)
dist = Categorical(action_probs)
#action = dist.sample()
action = torch.argmax(dist.probs)
action_logprob = dist.log_prob(action)
return action.detach(), action_logprob.detach()
def evaluate(self, observations, state, action):
if self.has_continuous_action_space:
action_mean = 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)
# For Single Action Environments.
if self.action_dim == 1:
action = action.reshape(-1, self.action_dim)
else:
action_probs = self.actor(state)
dist = Categorical(action_probs)
action_logprobs = dist.log_prob(action)
dist_entropy = dist.entropy()
state_values = self.critic(observations)
return action_logprobs, state_values, dist_entropy
def converge_eval(self, state, threshold=0.6):
idx = 0
converge_idx = -1
while state[0][-1]==0:
action_probs = self.actor(state)
temp = torch.argmax(action_probs)
if action_probs[0][temp] > threshold:
converge_idx = idx
state[0][idx] = temp+1
idx += 1
return converge_idx
class PPO:
def __init__(self, nagents, state_dim, action_dim, lr_actor, lr_critic, gamma, K_epochs, eps_clip, has_continuous_action_space, action_std_init=0.6,device=device):
self.has_continuous_action_space = has_continuous_action_space
if has_continuous_action_space:
self.action_std = action_std_init
self.gamma = gamma
self.eps_clip = eps_clip
self.K_epochs = K_epochs
self.buffer = RolloutBuffer()
self.pbuffer = dict() #permanant buffer
self.policy = ActorCritic(nagents, state_dim, action_dim, has_continuous_action_space, action_std_init,device=device).to(device)
self.optimizer = torch.optim.Adam([
{'params': self.policy.actor.parameters(), 'lr': lr_actor},
{'params': self.policy.critic.parameters(), 'lr': lr_critic}
], amsgrad=True)#, weight_decay=1e-4, betas=(0.85, 0.99)
self.policy_old = ActorCritic(nagents, state_dim, action_dim, has_continuous_action_space, action_std_init,device=device).to(device)
self.policy_old.load_state_dict(self.policy.state_dict())
self.MseLoss = nn.MSELoss()
def set_action_std(self, new_action_std):
if self.has_continuous_action_space:
self.action_std = new_action_std
self.policy.set_action_std(new_action_std)
self.policy_old.set_action_std(new_action_std)
else:
print("--------------------------------------------------------------------------------------------")
print("WARNING : Calling PPO::set_action_std() on discrete action space policy")
print("--------------------------------------------------------------------------------------------")
def decay_action_std(self, action_std_decay_rate, min_action_std):
print("--------------------------------------------------------------------------------------------")
if self.has_continuous_action_space:
self.action_std = self.action_std - action_std_decay_rate
self.action_std = round(self.action_std, 4)
if (self.action_std <= min_action_std):
self.action_std = min_action_std
print("setting actor output action_std to min_action_std : ", self.action_std)
else:
print("setting actor output action_std to : ", self.action_std)
self.set_action_std(self.action_std)
else:
print("WARNING : Calling PPO::decay_action_std() on discrete action space policy")
print("--------------------------------------------------------------------------------------------")
def select_action(self, obs, idx):
state = obs[idx]
if self.has_continuous_action_space:
with torch.no_grad():
state = torch.FloatTensor(state).to(device)
obs = torch.FloatTensor(obs.flatten()).to(device)
action, action_logprob = self.policy_old.act(state)
self.buffer.observations.append(obs)
self.buffer.states.append(state)
self.buffer.actions.append(action)
self.buffer.logprobs.append(action_logprob)
return action.detach().cpu().numpy().flatten()
else:
with torch.no_grad():
state = torch.FloatTensor(state).to(device)
obs = torch.FloatTensor(obs.flatten()).to(device)
action, action_logprob = self.policy_old.act(state)
self.buffer.observations.append(obs)
self.buffer.states.append(state)
self.buffer.actions.append(action)
self.buffer.logprobs.append(action_logprob)
return action.item()
def select_action2(self, obs, idx):
state = obs[idx]
if self.has_continuous_action_space:
with torch.no_grad():
state = torch.FloatTensor(state).to(device)
obs = torch.FloatTensor(obs.flatten()).to(device)
action, action_logprob = self.policy_old.act2(state)
self.buffer.observations.append(obs)
self.buffer.states.append(state)
self.buffer.actions.append(action)
self.buffer.logprobs.append(action_logprob)
return action.detach().cpu().numpy().flatten()
else:
with torch.no_grad():
state = torch.FloatTensor(state).to(device)
obs = torch.FloatTensor(obs.flatten()).to(device)
action, action_logprob = self.policy_old.act2(state)
self.buffer.observations.append(obs)
self.buffer.states.append(state)
self.buffer.actions.append(action)
self.buffer.logprobs.append(action_logprob)
return action.item()
'''def fast_gradient_sign_method(model, imgs, labels, epsilon=0.02):
# Determine prediction of the model
inp_imgs = imgs.clone().requires_grad_()
preds = model(inp_imgs.to(device))
preds = F.log_softmax(preds, dim=-1)
# Calculate loss by NLL
loss = -torch.gather(preds, 1, labels.to(device).unsqueeze(dim=-1))
loss.sum().backward()
# Update image to adversarial example as written above
noise_grad = torch.sign(inp_imgs.grad.to(imgs.device))
fake_imgs = imgs + epsilon * noise_grad
fake_imgs.detach_()
return fake_imgs, noise_grad'''
def fgsm(self, epsilon=0.2):
# Monte Carlo estimate of returns
rewards = []
discounted_reward = 0
for reward, is_terminal in zip(reversed(self.buffer.rewards), reversed(self.buffer.is_terminals)):
if is_terminal:
discounted_reward = 0
discounted_reward = reward + (self.gamma * discounted_reward)
rewards.insert(0, discounted_reward)
for i in range(len(self.buffer.states)):
try:
rewards[i] = self.pbuffer[self.buffer.states[i]] = max(self.pbuffer[self.buffer.states[i]],rewards[i])#rewards[i] =
except KeyError:
self.pbuffer[self.buffer.states[i]] = rewards[i]
#rewards_all = torch.tensor(list(self.pbuffer.values()), dtype=torch.float32).to(device)
# Normalizing the rewards
rewards = torch.tensor(rewards, dtype=torch.float32).to(device)
rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-7) #use global mean and std
# convert list to tensor
old_observations = torch.squeeze(torch.stack(self.buffer.observations, dim=0)).clone().requires_grad_().to(device)
old_states = torch.squeeze(torch.stack(self.buffer.states, dim=0)).clone().requires_grad_().to(device)
old_actions = torch.squeeze(torch.stack(self.buffer.actions, dim=0)).detach().to(device)
old_logprobs = torch.squeeze(torch.stack(self.buffer.logprobs, dim=0)).detach().to(device)
# Optimize policy for K epochs
observations_grads = []
states_grads = []
for _ in range(self.K_epochs):
# Evaluating old actions and values
logprobs, state_values, dist_entropy = self.policy.evaluate(old_observations, old_states, old_actions)
# match state_values tensor dimensions with rewards tensor
state_values = torch.squeeze(state_values)
# 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
# final loss of clipped objective PPO
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()
observations_grads.append(old_observations.grad.to(old_observations.device))
states_grads.append(old_states.grad.to(old_states.device))
og_sign = torch.sign(torch.sum(torch.stack(observations_grads), dim=0))
sg_sign = torch.sign(torch.sum(torch.stack(states_grads), dim=0))
fake_observations = old_observations + epsilon * og_sign
fake_states = old_states + epsilon * sg_sign
fake_observations.detach_()
fake_states.detach_()
for _ in range(self.K_epochs):
# Evaluating old actions and values
logprobs, state_values, dist_entropy = self.policy.evaluate(fake_observations, fake_states, old_actions)
# match state_values tensor dimensions with rewards tensor
state_values = torch.squeeze(state_values)
# 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
# final loss of clipped objective PPO
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())
# clear buffer
self.buffer.clear()
#return fake_observations, fake_states
def update(self, threshold=0.6):
# Monte Carlo estimate of returns
rewards = []
discounted_reward = 0
for reward, is_terminal in zip(reversed(self.buffer.rewards), reversed(self.buffer.is_terminals)):
if is_terminal:
discounted_reward = 0
discounted_reward = reward + (self.gamma * discounted_reward)
rewards.insert(0, discounted_reward)
for i in range(len(self.buffer.states)):
try:
rewards[i] = self.pbuffer[self.buffer.states[i]] = max(self.pbuffer[self.buffer.states[i]],rewards[i])#rewards[i] =
except KeyError:
self.pbuffer[self.buffer.states[i]] = rewards[i]
#rewards_all = torch.tensor(list(self.pbuffer.values()), dtype=torch.float32).to(device)
# Normalizing the rewards
rewards = torch.tensor(rewards, dtype=torch.float32).to(device)
rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-7) #use global mean and std
# convert list to tensor
old_observations = torch.squeeze(torch.stack(self.buffer.observations, dim=0)).detach().to(device)
old_states = torch.squeeze(torch.stack(self.buffer.states, dim=0)).detach().to(device)
old_actions = torch.squeeze(torch.stack(self.buffer.actions, dim=0)).detach().to(device)
old_logprobs = torch.squeeze(torch.stack(self.buffer.logprobs, dim=0)).detach().to(device)
# 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_observations, old_states, old_actions)
# match state_values tensor dimensions with rewards tensor
state_values = torch.squeeze(state_values)
# 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
# final loss of clipped objective PPO
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())
# clear buffer
self.buffer.clear()
#init_state = torch.FloatTensor([[0]*8]).detach().to(device)
#convergence_eval = self.policy.converge_eval(init_state, threshold=threshold)
#return convergence_eval
def save(self, checkpoint_path):
torch.save(self.policy_old.state_dict(), checkpoint_path)
def load(self, checkpoint_path):
self.policy_old.load_state_dict(torch.load(checkpoint_path, map_location=lambda storage, loc: storage))
self.policy.load_state_dict(torch.load(checkpoint_path, map_location=lambda storage, loc: storage))