The REINFORCE algorithm is significant because it is one of the simplest and most direct implementations of policy gradient methods in reinforcement learning. REINFORCE updates the policy parameters by estimating the gradient of the expected return with respect to the policy, using Monte Carlo sampling to compute the return.
The algorithm works by generating trajectories (episodes) and then calculating the total reward for each trajectory. The policy parameters are updated to increase the probability of actions that lead to higher rewards, using the following update rule: θ ← θ + α * ∇θ log π(a