Reinforcement Learning Part 13: Policy Gradient Causality Trick and REINFORCE

In the previous post, we showed how we can substitute our usual ε-greedy policy with a parameterized approximation (often a neural network), we then derived the policy gradient theorem required to optimize this approximator function, and demonstrated how it can be estimated using Monte Carlo sampling. At the very end, we pointed out that the […]