# What is the loss for policy gradients with continuous actions?

I know with policy gradients used in an environment with a discrete action space are updated with $$\Delta \theta_{t}=\alpha \nabla_{\theta} \log \pi_{\theta}\left(a_{t} \mid s_{t}\right) v_{t}$$ where $$v_t$$ could be many things that represent how good the action was. And I know that this can be calculated by performing cross entropy loss with the target being what the network would have outputted if it were completely confident in its action (zeros with the index of the action chosen being one). But I don’t understand how to apply that to policy gradients that output the mean and variance of a Gaussian distribution for a continuous action space. What is the loss for these types of policy gradients?

I tried keeping the variance constant and updating the output with mean squared error loss and the target being the action it took. I thought this would end up pushing the mean towards actions with greater total rewards but it got nowhere in OpenAI’s Pendulum environment.

It would also be very helpful if it was described in a way with a loss function and a target, like how policy gradients with discrete action spaces can be updated with cross entropy loss. That is how I understand it best but it is okay if that is not possible.

Edit: for @Philipp. The way I understand it is that the loss function is the same with a continuous action space and the only thing that changes is the distribution that we get the log-probs from. In PyTorch we can use a Normal distribution for continuous action space and Categorical for discrete action space. The answer from David Ireland goes into the math but in PyTorch, that looks like log_prob = distribution.log_prob(action_taken) for any type of distribution. It makes sense that for bad actions we would want to decrease the probability of taking the action. Below is working code for both types of action spaces to compare them. The continuous action space code should be correct but the agent will not learn because it is harder to learn the right actions with a continuous action space and our simple method isn't enough. Look into more advanced methods like PPO and DDPG.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.categorical import Categorical #discrete distribution
import numpy as np
import gym
import math
import matplotlib.pyplot as plt

class Agent(nn.Module):
def __init__(self,lr):
super(Agent,self).__init__()
self.fc1 = nn.Linear(4,64)
self.fc2 = nn.Linear(64,32)
self.fc3 = nn.Linear(32,2) #neural network with layers 4,64,32,2

def forward(self,x):
x = torch.relu(self.fc1(x)) #relu and tanh for output
x = torch.relu(self.fc2(x))
x = torch.sigmoid(self.fc3(x))
return x

env = gym.make('CartPole-v0')
agent = Agent(0.001) #hyperparameters
DISCOUNT = 0.99
total = []

for e in range(500):
log_probs, rewards = [], []
done = False
state = env.reset()
while not done:
#mu = agent.forward(torch.from_numpy(state).float())
#distribution = Normal(mu, SIGMA)
distribution = Categorical(agent.forward(torch.from_numpy(state).float()))
action = distribution.sample()
log_probs.append(distribution.log_prob(action))
state, reward, done, info = env.step(action.item())
rewards.append(reward)

total.append(sum(rewards))

cumulative = 0
d_rewards = np.zeros(len(rewards))
for t in reversed(range(len(rewards))): #get discounted rewards
cumulative = cumulative * DISCOUNT + rewards[t]
d_rewards[t] = cumulative
d_rewards -= np.mean(d_rewards) #normalize
d_rewards /= np.std(d_rewards)

loss = 0
for t in range(len(rewards)):
loss += -log_probs[t] * d_rewards[t] #loss is - log prob * total reward

loss.backward() #update
agent.optimizer.step()

if e%10==0:
print(e,sum(rewards))
plt.plot(total,color='blue') #plot
plt.pause(0.0001)

def run(i): #to visualize performance
for _ in range(i):
done = False
state = env.reset()
while not done:
env.render()
distribution = Categorical(agent.forward(torch.from_numpy(state).float()))
action = distribution.sample()
state,reward,done,info = env.step(action.item())
env.close()


Above is the discrete action space code for CartPole and below is the continuous action space code for Pendulum. Sigma (variance or standard deviation) is constant here but adding it is easy. Just make the final layer have two neurons and make sure sigma is not negative. Again, the pendulum code won't work because most environments with continuous action spaces are too complicated for such a simple method. Making it work would probably require a lot of testing for hyper parameters.

import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.normal import Normal #continuous distribution
import numpy as np
import gym
import math
import matplotlib.pyplot as plt
import keyboard

class Agent(nn.Module):
def __init__(self,lr):
super(Agent,self).__init__()
self.fc1 = nn.Linear(3,64)
self.fc2 = nn.Linear(64,32)
self.fc3 = nn.Linear(32,1) #neural network with layers 3,64,32,1

def forward(self,x):
x = torch.relu(self.fc1(x)) #relu and tanh for output
x = torch.relu(self.fc2(x))
x = torch.tanh(self.fc3(x)) * 2
return x

env = gym.make('Pendulum-v0')
agent = Agent(0.01) #hyperparameters
SIGMA = 0.2
DISCOUNT = 0.99
total = []

for e in range(1000):
log_probs, rewards = [], []
done = False
state = env.reset()
while not done:
mu = agent.forward(torch.from_numpy(state).float())
distribution = Normal(mu, SIGMA)
action = distribution.sample().clamp(-2.0,2.0)
log_probs.append(distribution.log_prob(action))
state, reward, done, info = env.step([action.item()])
#reward = abs(state[1])
rewards.append(reward)

total.append(sum(rewards))

cumulative = 0
d_rewards = np.zeros(len(rewards))
for t in reversed(range(len(rewards))): #get discounted rewards
cumulative = cumulative * DISCOUNT + rewards[t]
d_rewards[t] = cumulative
d_rewards -= np.mean(d_rewards) #normalize
d_rewards /= np.std(d_rewards)

loss = 0
for t in range(len(rewards)):
loss += -log_probs[t] * d_rewards[t] #loss is - log prob * total reward

loss.backward() #update
agent.optimizer.step()

if e%10==0:
print(e,sum(rewards))
plt.plot(total,color='blue') #plot
plt.pause(0.0001)
if keyboard.is_pressed("space"): #holding space exits training
raise Exception("Exited")

def run(i): #to visualize performance
for _ in range(i):
done = False
state = env.reset()
while not done:
env.render()
distribution = Normal(agent.forward(torch.from_numpy(state).float()), SIGMA)
action = distribution.sample()
state,reward,done,info = env.step([action.item()])
env.close()


David Ireland also wrote this on a different question I had:

The algorithm doesn't change in this situation. Say your NN outputs the mean parameter of the Gaussian, then logπ(at|st) is just the log of the normal density evaluated at the action you took where the mean parameter in the density is the output of your NN. You are then able to backpropagate through this to update the weights of your network.

• You might want to first express your policy as a density function over the space of possible actions. If you have one of these for each state, and the states are a continuous space as well, you might want to look at Gaussian processes and the likes. Sep 30, 2020 at 22:36
• No, you don't want it to be discrete. A policy over a continuous space is necessarily a probability density function. With enough training, this should converge to a Dirac delta function centered on the optimal action. If you parameterize a density, you can use that in the formula you have, and update the parameters according to the gradient. Oct 1, 2020 at 5:49
• @S2673 Did you eventually manage to implement working code from the answer? I find it very difficult to wrap my head around the accepted answer.. Mar 30, 2021 at 16:59
• @Philipp I did get it working and I can’t right now but soon I will look at my code to remember how I solved it. Mar 31, 2021 at 2:37
• @Philipp Sorry it took so long but I finally edited my question to try to help you. I had forgotten a lot of this code. I'm not sure exactly what part you were having trouble with so you can ask more questions if you need. After getting this right, you can code more advanced algorithms like PPO that can actually solve the environments. And I saw the question on your profile, I also thought that I could keep using cross-entropy loss in continuous environments but I don't know if it works because there is not really a clear target. Apr 3, 2021 at 21:52

This update rule can still be applied in the continuous domain.

As pointed out in the comments, suppose we are parameterising our policy using a Gaussian distribution, where our neural networks take as input the state we are in and output the parameters of a Gaussian distribution, the mean and the standard deviation which we will denote as $$\mu(s, \theta)$$ and $$\sigma(s, \theta)$$ where $$s$$ shows the dependancy of the state and $$\theta$$ are the parameters of our network.

I will assume a one-dimensional case for ease of notation but this can be extended to multi-variate cases. Our policy is now defined as $$\pi(a_t | s_t) = \frac{1}{\sqrt{2\pi \sigma(s_t, \theta)^2}} \exp\left(-\frac{1}{2}\left(\frac{a_t - \mu(s_t, \theta)}{\sigma(s_t, \theta)}\right)^2\right).$$

As you can see, we can easily take the logarithm of this and find the derivative with respect to $$\theta$$, and so nothing changes and the loss you use is the same. You simply evaluate the derivative of the log of your policy with respect to the network parameters, multiply by $$v_t$$ and $$\alpha$$ and take a gradient step in this direction.

To implement this (as I'm assuming you don't want to calculate the NN derivatives by hand) then you could do something along the lines of the following in Pytorch.

First you want to pass your state through your NN to get the mean and standard deviation of the Gaussian distribution. Then you want to simulate $$z \sim N(0,1)$$ and calculate $$a = \mu(s,\theta) + \sigma(s, \theta) \times z$$ so that $$a \sim N( \mu(s, \theta), \sigma(s, \theta))$$ -- this is the reparameterisation trick that makes backpropagation through the network easier as it takes the randomness from a source that doesn't depend on the parameters of the network. $$a$$ is your action that you will execute in your environment and use to calculate the gradient by simply writing the code torch.log(normal_pdf(a, \mu(s, \theta), \sigma(s, \theta)).backward() -- here normal_pdf() is any function in Python that calculates the pdf of a normal distribution for a given point and parameters.

• A log-normal distribution is not the same as taking the log of the pdf of the normal distribution, it is taking the log of the random variable. Mar 31, 2021 at 11:08
• Ah sorry, also just realized that and deleted my comment, but thanks! Mar 31, 2021 at 11:09
• I imagine that when you have seen things like tanh for mu it is for stability. For sigma it is necessary that sigma is non-zero so you will need to force it to be non-zero. I am not an expert numerical stability of neural networks and such but I have had problems in the past, in particular when predicting sigma, is that it is very unstable. What I have seen people do (as usually in ML you end up taking the log of a density) is to directly output the log of the variance, this way you don't need to worry about it being strictly bigger than 0. Apr 1, 2021 at 11:56
• you can see in the code I attached that I take backward() of the log of the pdf. I could similarly have called loss = torch.log(normal_pdf(a, \mu(s, \theta), \sigma(s, \theta)) and then called loss.backward() and this would be using the more traditional variable names. Note that when you do optimiser.step() gradient descent is performed so I should technically call loss = -torch.log(normal_pdf(a, \mu(s, \theta), \sigma(s, \theta)) as then minimising this will be equivalent to maximising the RL objective. Please let me know if this makes sense. Apr 1, 2021 at 12:05
• @Philipp My advice would be to not think about curve fitting when thinking of RL, it is a very different paradigm to supervised/unsupervised learning. My main advice would be to make sure you go through all the theory (Sutton and Barto is always my go to recommendation) so that you understand why we do what we do. The reason we take the log of the policy is because of how the policy gradient is derived; it is difficult to explain in this little comment section but if you follow the derivation of the policy gradient and how we end up at the REINFORCE update rule it'll make sense. Apr 4, 2021 at 19:55