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SAC_PER.py
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SAC_PER.py
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import numpy as np
import random
import gym
from collections import deque
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal, MultivariateNormal
from torch.nn.utils import clip_grad_norm_
import torch.optim as optim
import gym.spaces
import argparse
from torch.utils.tensorboard import SummaryWriter
import time
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1. / np.sqrt(fan_in)
return (-lim, lim)
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, hidden_size=256, init_w=3e-3, log_std_min=-20, log_std_max=2):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
seed (int): Random seed
fc1_units (int): Number of nodes in first hidden layer
fc2_units (int): Number of nodes in second hidden layer
"""
super(Actor, self).__init__()
self.seed = torch.manual_seed(seed)
self.log_std_min = log_std_min
self.log_std_max = log_std_max
self.fc1 = nn.Linear(state_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.mu = nn.Linear(hidden_size, action_size)
self.log_std_linear = nn.Linear(hidden_size, action_size)
def reset_parameters(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.mu.weight.data.uniform_(-init_w, init_w)
self.log_std_linear.weight.data.uniform_(-init_w, init_w)
def forward(self, state):
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
mu = self.mu(x)
log_std = self.log_std_linear(x)
log_std = torch.clamp(log_std, self.log_std_min, self.log_std_max)
return mu, log_std
def evaluate(self, state, epsilon=1e-6):
mu, log_std = self.forward(state)
std = log_std.exp()
dist = Normal(0, 1)
e = dist.sample().to(device)
action = torch.tanh(mu + e * std)
log_prob = Normal(mu, std).log_prob(mu + e * std) - torch.log(1 - action.pow(2) + epsilon)
return action, log_prob
def get_action(self, state):
"""
returns the action based on a squashed gaussian policy. That means the samples are obtained according to:
a(s,e)= tanh(mu(s)+sigma(s)+e)
"""
mu, log_std = self.forward(state.unsqueeze(0))
std = log_std.exp()
dist = Normal(0, 1)
e = dist.sample().to(device)
action = torch.tanh(mu + e * std).cpu()
return action[0]
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, hidden_size=256):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
action_size (int): Dimension of each action
seed (int): Random seed
hidden_size (int): Number of nodes in the network layers
"""
super(Critic, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size+action_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear(hidden_size, 1)
self.reset_parameters()
def reset_parameters(self):
self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
self.fc3.weight.data.uniform_(-3e-3, 3e-3)
def forward(self, state, action):
"""Build a critic (value) network that maps (state, action) pairs -> Q-values."""
x = torch.cat((state, action), dim=1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
return self.fc3(x)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, random_seed, action_prior="uniform"):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.target_entropy = -action_size # -dim(A)
self.alpha = 1
self.log_alpha = torch.tensor([0.0], requires_grad=True)
self.alpha_optimizer = optim.Adam(params=[self.log_alpha], lr=LR_ACTOR)
self._action_prior = action_prior
print("Using: ", device)
# Actor Network
self.actor_local = Actor(state_size, action_size, random_seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic1 = Critic(state_size, action_size, random_seed).to(device)
self.critic2 = Critic(state_size, action_size, random_seed).to(device)
self.critic1_target = Critic(state_size, action_size, random_seed).to(device)
self.critic1_target.load_state_dict(self.critic1.state_dict())
self.critic2_target = Critic(state_size, action_size, random_seed).to(device)
self.critic2_target.load_state_dict(self.critic2.state_dict())
self.critic1_optimizer = optim.Adam(self.critic1.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY)
self.critic2_optimizer = optim.Adam(self.critic2.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY)
# Replay memory
self.memory = PrioritizedReplay(capacity=BUFFER_SIZE)
def step(self, state, action, reward, next_state, done, step):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# Save experience / reward
self.memory.push(state, action, reward, next_state, done)
# Learn, if enough samples are available in memory
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample(BATCH_SIZE)
self.learn(step, experiences, GAMMA)
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).unsqueeze(0).float().to(device)
action = self.actor_local.get_action(state).detach()
return action
def learn(self, step, experiences, gamma, d=1):
"""Updates actor, critics and entropy_alpha parameters using given batch of experience tuples.
Q_targets = r + γ * (min_critic_target(next_state, actor_target(next_state)) - α *log_pi(next_action|next_state))
Critic_loss = MSE(Q, Q_target)
Actor_loss = α * log_pi(a|s) - Q(s,a)
where:
actor_target(state) -> action
critic_target(state, action) -> Q-value
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones, idx, weights = experiences
states = torch.FloatTensor(np.float32(states)).to(device)
next_states = torch.FloatTensor(np.float32(next_states)).to(device)
actions = torch.cat(actions).to(device)
rewards = torch.FloatTensor(rewards).to(device).unsqueeze(1)
dones = torch.FloatTensor(dones).to(device).unsqueeze(1)
weights = torch.FloatTensor(weights).unsqueeze(1)
#print(actions.shape)
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
next_action, log_pis_next = self.actor_local.evaluate(next_states)
Q_target1_next = self.critic1_target(next_states.to(device), next_action.squeeze(0).to(device))
Q_target2_next = self.critic2_target(next_states.to(device), next_action.squeeze(0).to(device))
# take the mean of both critics for updating
Q_target_next = torch.min(Q_target1_next, Q_target2_next).cpu()
# Compute Q targets for current states (y_i)
Q_targets = rewards.cpu() + (gamma * (1 - dones.cpu()) * (Q_target_next - self.alpha * log_pis_next.mean(1).unsqueeze(1).cpu()))
# Compute critic loss
Q_1 = self.critic1(states, actions).cpu()
Q_2 = self.critic2(states, actions).cpu()
td_error1 = Q_targets.detach()-Q_1#,reduction="none"
td_error2 = Q_targets.detach()-Q_2
critic1_loss = 0.5* (td_error1.pow(2)*weights).mean()
critic2_loss = 0.5* (td_error2.pow(2)*weights).mean()
prios = abs(((td_error1 + td_error2)/2.0 + 1e-5).squeeze())
# Update critics
# critic 1
self.critic1_optimizer.zero_grad()
critic1_loss.backward()
self.critic1_optimizer.step()
# critic 2
self.critic2_optimizer.zero_grad()
critic2_loss.backward()
self.critic2_optimizer.step()
self.memory.update_priorities(idx, prios.data.cpu().numpy())
if step % d == 0:
# ---------------------------- update actor ---------------------------- #
alpha = torch.exp(self.log_alpha)
# Compute alpha loss
actions_pred, log_pis = self.actor_local.evaluate(states)
alpha_loss = - (self.log_alpha.cpu() * (log_pis.cpu() + self.target_entropy).detach().cpu()).mean()
self.alpha_optimizer.zero_grad()
alpha_loss.backward()
self.alpha_optimizer.step()
self.alpha = alpha
# Compute actor loss
if self._action_prior == "normal":
policy_prior = MultivariateNormal(loc=torch.zeros(self.action_size), scale_tril=torch.ones(self.action_size).unsqueeze(0))
policy_prior_log_probs = policy_prior.log_prob(actions_pred)
elif self._action_prior == "uniform":
policy_prior_log_probs = 0.0
actor_loss = ((alpha * log_pis.squeeze(0).cpu() - self.critic1(states, actions_pred.squeeze(0)).cpu() - policy_prior_log_probs )*weights).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic1, self.critic1_target, TAU)
self.soft_update(self.critic2, self.critic2_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)
class PrioritizedReplay(object):
"""
Proportional Prioritization
"""
def __init__(self, capacity, alpha=0.6,beta_start = 0.4,beta_frames=100000):
self.alpha = alpha
self.beta_start = beta_start
self.beta_frames = beta_frames
self.frame = 1 #for beta calculation
self.capacity = capacity
self.buffer = []
self.pos = 0
self.priorities = np.zeros((capacity,), dtype=np.float32)
def beta_by_frame(self, frame_idx):
"""
Linearly increases beta from beta_start to 1 over time from 1 to beta_frames.
3.4 ANNEALING THE BIAS (Paper: PER)
We therefore exploit the flexibility of annealing the amount of importance-sampling
correction over time, by defining a schedule on the exponent
that reaches 1 only at the end of learning. In practice, we linearly anneal from its initial value 0 to 1
"""
return min(1.0, self.beta_start + frame_idx * (1.0 - self.beta_start) / self.beta_frames)
def push(self, state, action, reward, next_state, done):
assert state.ndim == next_state.ndim
state = np.expand_dims(state, 0)
next_state = np.expand_dims(next_state, 0)
max_prio = self.priorities.max() if self.buffer else 1.0 # gives max priority if buffer is not empty else 1
if len(self.buffer) < self.capacity:
self.buffer.append((state, action, reward, next_state, done))
else:
# puts the new data on the position of the oldes since it circles via pos variable
# since if len(buffer) == capacity -> pos == 0 -> oldest memory (at least for the first round?)
self.buffer[self.pos] = (state, action, reward, next_state, done)
self.priorities[self.pos] = max_prio
self.pos = (self.pos + 1) % self.capacity # lets the pos circle in the ranges of capacity if pos+1 > cap --> new posi = 0
def sample(self, batch_size):
N = len(self.buffer)
if N == self.capacity:
prios = self.priorities
else:
prios = self.priorities[:self.pos]
# calc P = p^a/sum(p^a)
probs = prios ** self.alpha
P = probs/probs.sum()
#gets the indices depending on the probability p
indices = np.random.choice(N, batch_size, p=P)
samples = [self.buffer[idx] for idx in indices]
beta = self.beta_by_frame(self.frame)
self.frame+=1
#Compute importance-sampling weight
weights = (N * P[indices]) ** (-beta)
# normalize weights
weights /= weights.max()
weights = np.array(weights, dtype=np.float32)
states, actions, rewards, next_states, dones = zip(*samples)
return np.concatenate(states), actions, rewards, np.concatenate(next_states), dones, indices, weights
def update_priorities(self, batch_indices, batch_priorities):
for idx, prio in zip(batch_indices, batch_priorities):
self.priorities[idx] = abs(prio)
def __len__(self):
return len(self.buffer)
def SAC(n_episodes=200, max_t=1000, print_every=10):
scores_deque = deque(maxlen=args.print_every)
t = 0
for i_episode in range(1, n_episodes+1):
state = env.reset()
score = 0
while True:
t += 1
action = agent.act(state)
action_v = action[0].numpy()
action_v = np.clip(action_v*action_high, action_low, action_high)
next_state, reward, done, info = env.step(action_v)
agent.step(state, action, reward, next_state, done, t)
state = next_state
score += reward
if done:
break
scores_deque.append(score)
writer.add_scalar("Reward", score, i_episode)
writer.add_scalar("average_X", np.mean(scores_deque), i_episode)
print('\rEpisode {} Reward: {:.2f} Average100 Score: {:.2f}'.format(i_episode, score, np.mean(scores_deque)), end="")
if i_episode % print_every == 0:
print('\rEpisode {} Reward: {:.2f} Average100 Score: {:.2f}'.format(i_episode, score, np.mean(scores_deque)))
torch.save(agent.actor_local.state_dict(), args.info + ".pt")
def play():
agent.actor_local.eval()
for i_episode in range(1):
state = env.reset()
while True:
action = agent.act(state)
action_v = action[0].numpy()
action_v = np.clip(action_v*action_high, action_low, action_high)
next_state, reward, done, info = env.step(action_v)
next_state = next_state
state = next_state
if done:
break
parser = argparse.ArgumentParser()
parser.add_argument("-env", type=str, default="Pendulum-v0", help="Name of the Environment")
parser.add_argument("-ep", type=int, default=100, help="Number of Episodes to train, default = 100")
parser.add_argument("-bs", "--buffer_size", type=int, default=int(1e6), help="Size of the Replay buffer, default= 1e6")
parser.add_argument("-bsize", "--batch_size", type=int, default=256, help="Batch size for the optimization process, default = 256")
parser.add_argument("-seed", type=int, default=0, help="Seed for the env and torch network weights, default is 0")
parser.add_argument("-lr", type=float, default=5e-4, help="Learning Rate, default = 5e-4")
parser.add_argument("-g", type=float, default=0.99, help="discount factor gamma, default = 0.99")
parser.add_argument("-wd", type=float, default=0, help="Weight decay, default = 0")
parser.add_argument("-ls", "--layer_size", type=int, default=256, help="Number of nodes per neural network layer, default = 256")
parser.add_argument("--print_every", type=int, default=100, help="Prints every x episodes the average reward over x episodes")
parser.add_argument("-info", type=str, help="tensorboard test run information")
parser.add_argument("-device", type=str, default="cuda:0", help="Change to CPU computing or GPU, default=cuda:0")
parser.add_argument("--saved_model", type=str, default=None, help="Load a saved model to perform a test run!")
parser.add_argument("-t", "--tau", type=float, default=1e-2, help="Softupdate factor tau, default is 1e-2")
args = parser.parse_args()
if __name__ == "__main__":
seed = args.seed
BUFFER_SIZE = args.buffer_size
BATCH_SIZE = args.batch_size # minibatch size
GAMMA = args.g # discount factor
TAU = args.tau # for soft update of target parameters
LR_ACTOR = args.lr # learning rate of the actor
LR_CRITIC = args.lr # learning rate of the critic
WEIGHT_DECAY = args.wd#1e-2 # L2 weight decay
HIDDEN_SIZE = args.layer_size
saved_model = args.saved_model
env_name = args.env
device = args.device
env = gym.make(env_name)
env.seed(seed)
np.random.seed(seed)
state_size = env.observation_space.shape[0]
action_size = env.action_space.shape[0]
action_high = env.action_space.high[0]
action_low = env.action_space.low[0]
writer = SummaryWriter("runs/"+args.info)
agent = Agent(state_size=state_size, action_size=action_size, random_seed=seed, action_prior="uniform") #"normal"
start_time = time.time()
if saved_model != None:
agent.actor_local.load_state_dict(torch.load(saved_model))
play()
else:
SAC(n_episodes=args.ep, max_t=500 , print_every=args.print_every)
end_time = time.time()
env.close()
print("Training took: {} min".format((end_time-start_time)/60))
#writer.add_hparams()