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sac_continous_stochastic_entropy.py
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sac_continous_stochastic_entropy.py
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# Haarnoja, Tuomas, et al. "Soft actor-critic algorithms and applications." arXiv preprint arXiv:1812.05905 (2018).
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as D
import numpy as np
import gym
import random
from collections import deque
class ReplayBuffer():
def __init__(self, max_size=100000):
super(ReplayBuffer, self).__init__()
self.max_size = max_size
self.memory = deque(maxlen=self.max_size)
# Add the replay memory
def add(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
# Sample the replay memory
def sample(self, batch_size):
batch = random.sample(self.memory, min(batch_size, len(self.memory)))
states, actions, rewards, next_states, dones = map(np.stack, zip(*batch))
return states, actions, rewards, next_states, dones
class SoftQNet(nn.Module):
def __init__(self, state_num, action_num):
super(SoftQNet, self).__init__()
# Network 1
self.input1 = nn.Linear(state_num + action_num, 512)
self.fc1 = nn.Linear(512, 512)
self.output1 = nn.Linear(512, 1)
# Network 2
self.input2 = nn.Linear(state_num + action_num, 512)
self.fc2 = nn.Linear(512, 512)
self.output2 = nn.Linear(512, 1)
def forward(self, x, u):
# Network 1
x1 = torch.cat([x, u], 1)
x1 = F.relu(self.input1(x1))
x1 = F.relu(self.fc1(x1))
value1 = self.output1(x1)
# Network 2
x2 = torch.cat([x, u], 1)
x2 = F.relu(self.input2(x2))
x2 = F.relu(self.fc2(x2))
value2 = self.output2(x2)
return value1, value2
class PolicyNet(nn.Module):
def __init__(self, state_num, min_action, max_action):
super(PolicyNet, self).__init__()
self.min_action = min_action
self.max_action = max_action
self.input = nn.Linear(state_num, 512)
self.fc1 = nn.Linear(512, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, 512)
self.mu = nn.Linear(512, 1)
self.std = nn.Linear(512, 1)
def forward(self, x):
x = F.relu(self.input(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
mu = (self.max_action - self.min_action) * F.sigmoid(self.mu(x)) + self.min_action
std = (self.max_action - self.min_action) * F.sigmoid(self.std(x)) / 2
# mu = self.mu(x).clamp(min=self.min_action, max=self.max_action)
# std = F.softplus(self.std(x)) # eliminate nagative value
return mu, std
# Enforcing action bounds
def sample(self, states, epsilon=1e-6):
mu, std = self.forward(states)
dist = D.Normal(mu, std)
actions = dist.rsample()
log_probs = dist.log_prob(actions) - torch.log(1. - torch.tanh(actions).pow(2) + epsilon)
return actions, log_probs
class SAC():
def __init__(self, env, memory_size=1000000, batch_size=64, gamma=0.95, learning_rate=1e-3, tau=0.01, target_entropy=-1, reward_normalization=False, reward_scale=5):
super(SAC, self).__init__()
self.env = env
self.state_num = self.env.observation_space.shape[0]
self.action_num = self.env.action_space.shape[0]
self.action_max = float(env.action_space.high[0])
self.action_min = float(env.action_space.low[0])
# Torch
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Soft Q
self.soft_q_net = SoftQNet(self.state_num, self.action_num).to(self.device)
self.soft_q_opt = optim.Adam(self.soft_q_net.parameters(), lr=learning_rate)
# Soft Q target
self.soft_q_target_net = SoftQNet(self.state_num, self.action_num).to(self.device)
self.soft_q_target_net.load_state_dict(self.soft_q_net.state_dict())
# Policy
self.policy_net = PolicyNet(self.state_num, self.action_min, self.action_max).to(self.device)
self.policy_opt = optim.Adam(self.policy_net.parameters(), lr=learning_rate)
# Temperature parameter alpha
self.alpha = torch.ones(1, dtype=torch.float32, requires_grad=True, device=self.device)
self.alpha_opt = optim.Adam([self.alpha], lr=learning_rate)
self.target_entropy = target_entropy
# Replay buffer
self.replay_buffer = ReplayBuffer(memory_size)
self.batch_size = batch_size
# Learning setting
self.gamma = gamma
self.tau = tau
# Reward setting
self.reward_normalization = reward_normalization
self.reward_scale = reward_scale
# Get the action
def get_action(self, state):
state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
mu, std = self.policy_net(state)
action = D.Normal(mu, std).sample()
action = action.cpu().detach().numpy()
return action[0]
# Soft update a target network
def soft_update(self, net, target_net):
for param, target_param in zip(net.parameters(), target_net.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
def learn(self):
# Get memory from replay buffer
states, actions, rewards, next_states, dones = self.replay_buffer.sample(self.batch_size)
states = torch.FloatTensor(states).to(self.device)
actions = torch.FloatTensor(actions).to(self.device)
rewards = torch.FloatTensor(rewards).to(self.device).view(-1, 1)
next_states = torch.FloatTensor(next_states).to(self.device)
dones = torch.FloatTensor(dones).to(self.device).view(-1, 1)
# Reward normalization and scailing
rewards = self.reward_scale * (rewards - rewards.mean()) / rewards.std() if self.reward_normalization else rewards
# Get the new next action with the current policy
new_next_actions, next_log_probs = self.policy_net.sample(next_states)
# Get the target q value
next_q_value_1, next_q_value_2 = self.soft_q_target_net(next_states, new_next_actions)
next_q_values = torch.min(next_q_value_1, next_q_value_2)
target_q_values = rewards + self.gamma * (next_q_values - self.alpha * next_log_probs) * (1-dones)
# Calculate the q value loss and optimize the q value network
q_value_1, q_value_2 = self.soft_q_net(states, actions)
q_loss = F.mse_loss(q_value_1, target_q_values.detach()) + F.mse_loss(q_value_2, target_q_values.detach())
self.soft_q_opt.zero_grad()
q_loss.backward()
self.soft_q_opt.step()
# Get the new action with the current policy
new_actions, log_probs = self.policy_net.sample(states)
# Get the minimum q value
new_q_value_1, new_q_value_2 = self.soft_q_net(states, new_actions)
q_values = torch.min(new_q_value_1, new_q_value_2)
# Calculate the policy loss and optimize the policy network
policy_loss = (self.alpha * log_probs - q_values).mean()
self.policy_opt.zero_grad()
policy_loss.backward()
self.policy_opt.step()
# Automating Entropy Adjustment for Maximum Entropy
alpha_loss = (-self.alpha * log_probs.detach() - self.alpha * self.target_entropy).mean()
self.alpha_opt.zero_grad()
alpha_loss.backward()
self.alpha_opt.step()
# Soft update the value network
self.soft_update(self.soft_q_net, self.soft_q_target_net)
def main():
env = gym.make("Pendulum-v0")
agent = SAC(env, memory_size=1000000, batch_size=128, gamma=0.99, learning_rate=3e-4, tau=0.01, target_entropy=-1, reward_normalization=True, reward_scale=10)
ep_rewards = deque(maxlen=1)
total_episode = 10000
for i in range(total_episode):
state = env.reset()
ep_reward = 0
while True:
action = agent.get_action(state)
next_state, reward , done, _ = env.step(action)
ep_reward += reward
agent.replay_buffer.add(state, action, reward, next_state, done)
if i > 2:
agent.learn()
if done:
ep_rewards.append(ep_reward)
if i % 1 == 0:
print("episode: {}\treward: {}".format(i, round(np.mean(ep_rewards), 3)))
break
state = next_state
if __name__ == '__main__':
main()