REINFORCE is a policy gradient algorithm used in reinforcement learning to optimize an agent’s behavior by directly adjusting its policy parameters. Unlike value-based methods that focus on estimating the value of actions or states, REINFORCE updates the policy—the strategy the agent uses to select actions—by following the gradient of expected rewards. It is model-free, meaning it does not require prior knowledge of the environment dynamics, and it works well in environments with continuous action spaces, where traditional Q-learning approaches might struggle. The algorithm is foundational for understanding how policy gradients operate and serves as a basis for more advanced techniques.
The core idea of REINFORCE involves calculating the gradient of the policy’s performance with respect to its parameters. For each action taken by the agent, the algorithm computes the gradient of the log probability of that action under the current policy, multiplies it by the reward received, and adjusts the policy parameters in the direction that increases the likelihood of high-reward actions. For example, if a robot learns to navigate a maze, actions that lead to reaching the goal faster are reinforced by scaling their probabilities based on the cumulative reward. REINFORCE uses Monte Carlo sampling, meaning it waits until the end of an episode to update the policy using the total rewards from that episode. To reduce variance in updates, a baseline (like the average reward) is often subtracted from the total reward, which helps stabilize training without introducing bias.
While REINFORCE is conceptually simple, it has practical limitations. Its reliance on full episode trajectories can lead to high variance in gradient estimates, making training slow or unstable. Developers often address this by combining it with techniques like neural networks for function approximation or using advanced optimizers like Adam. For instance, training a character in a game to perform complex movements might require thousands of episodes with careful tuning of learning rates. Despite these challenges, REINFORCE remains useful for prototyping and scenarios where precise value estimation is difficult. It is also a stepping stone to more sophisticated algorithms like Actor-Critic methods, which combine policy gradients with value function estimation for better performance.