Can I combine Monte Carlo policy gradient algorithm with other policy gradient algorithms
I know that Monte Carlo REINFORCE policy gradient algorithm is different in how it calculates the reward values by calculating discounted cumulative future reward at each step. 

here is the peace of code to calculate the discounted cumulative future reward at each time step.

G = np.zeros_like(self.reward_memory, dtype=np.float64)
        for t in range(len(self.reward_memory)):
            G_sum = 0
            discount = 1
            for k in range(t, len(self.reward_memory)):
                G_sum += self.reward_memory[k] * discount
                discount *= self.gamma
            G[t] = G_sum


another example for increasing accuracy is to calculate the reward after the action called "reward to go".
another example is to add the entropy bonus.

Is it possible to add the entropy bonus and the rewards to go or either one to the Monte Carlo method.

Also another step is taken in the Monte Carlo after the reward calculation is to normalize the values.


  “In practice it can can also be important to normalize these. For example, suppose we compute [discounted cumulative reward] for all of the 20,000 actions in the batch of 100 Pong game rollouts above. One good idea is to “standardize” these returns (e.g. subtract mean, divide by standard deviation) before we plug them into backprop. This way we’re always encouraging and discouraging roughly half of the performed actions. Mathematically you can also interpret these tricks as a way of controlling the variance of the policy gradient estimator”.


Does that effect the accuracy if both or either of the entropy bonus or reward to go modification is added?

That is from the research PDF https://arxiv.org/pdf/1506.02438.pdf

I am Studying Policy Gradient Algorithms and I want to know how to Improve these algorithms. I would greatly appreciated if you could help me out.

Edit:

I would also like to add on whether the advantage function could also be added 



The A(s,a) is the advantages function; is it possible to add this to the Monte Carlo approach assuming we also add both reward to go and the entropy bonus?

You are mixing some things up here.

The Monte Carlo approach is a way to compute the returns for the state-action pairs: as the discounted sum of all the future rewards after that state-action pair (s, a) following the current policy π.
(It is also worth noting that REINFORCE is not an especially good RL algorithm, and that Monte Carlo estimates of the returns have a rather high variance in comparison to e. g. TD(λ).)

The entropy bonus and the advantage function on the other hand are part of the loss (the function you use to train your actor), and therefore have nothing to do with the return computation.

I would suggest you read the Reinforcement Learning Book to get a deeper understanding of what you're doing.
