Taking 'done' into consideration while calculating returns

[murtazabasu]

Hello, thank you for making this repo, I think while calculating the returns you should take done into consideration as,

    def calculate_returns(self, rewards, dones, normalize = True):

        returns = []
        R = 0
        for r, d in zip(reversed(rewards), reversed(dones)):    
            if d:
                R = 0
            R = r + R * self.gamma
            returns.insert(0, R)

        returns = torch.tensor(returns).to(device)

        if normalize:
            returns = (returns - returns.mean()) / returns.std()

        return returns

Also can you please briefly describe the Generalized Advantage Estimation (GAE) while calculating the advantages.

[bentrevett]

Notebooks 1-7 all use Monte Carlo methods. That is each environment is run for a single episode, i.e. until the environment returns done = True, after which we then calculate the returns/advantages and update the policy parameters.

There is no need to check for done in the calculation of the returns/advantages as only the last state will have done = True, which is why R is initialized to zero.

I'll add the explanation to GAE when I get around to adding more detail to the notebooks - for now I'd recommend these two links:

• https://datascience.stackexchange.com/questions/32480/how-does-generalised-advantage-estimation-work
• https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html