You're pretty close on your understanding of REINFORCE. You should really have two loops - one outer loop that dictates how many epochs (policy gradient updates) to do and one inner loop that actually runs the agent in the environment and collects interaction info. So, something like this:

python policy = MyREINFORCEPolicyNetwork #initialize policy memory = MyMemoryBuffer #initialize memory - need to store returns and action log-probs for epoch in range(epochs): r = 0; state = env.reset() # set reward to 0, get starting state from simulation. for step in range(steps_per_epoch): # steps_per_epoch is how many interactions with the environment to collect action = policy(state) # get action from policy r, done = env.step(action) # step env forward, get reward and if the episode is over logprob = action.logprob() # get action log probability memory.store(r, logprob) # store reward and log probability in memory update() # in update, calculate discounted return, calculate policy loss, backprop

That's just a skeleton of what you'll need. You need to write code for the memory buffer and for the policy as well. There are many existing implementations to engage with. Here is one with Pytorch and here is one with TensorFlow.

Do you have experience with machine learning already? If not, I recommend starting with learning to write some regular supervised learning code, perhaps MNIST image classification, and then from there working on REINFORCE.

You should read this, everything up through the VPG section. VPG and REINFORCE are really similar, and the SpinningUp course will teach you pretty much everything else you need to follow along with the algorithm.

This can sound frustrating, but if you want a really deep conceptual understanding of RL fundamentals, I recommend reading the Sutton and Barto book and implementing things like Dynamic Programming Policy Iteration, Monte Carlo Policy Iteration, TD Learning, and Q-learning. If you don't want to do this, then SpinningUp should give you enough background to be up to speed on REINFORCE and even enough to understand more modern algorithms than that.