The REINFORCE algorithm is a foundational method in reinforcement learning (RL) that enables agents to learn policies directly by optimizing the parameters of a policy model. Unlike value-based methods, which focus on estimating the expected rewards of actions (e.g., Q-learning), REINFORCE operates by adjusting the policy to maximize the expected cumulative reward through gradient ascent. This makes it a policy gradient algorithm. For example, if an agent is learning to play a game, REINFORCE updates the probability of taking specific actions in certain states based on how much those actions contributed to higher overall rewards in past episodes. This direct approach allows it to handle environments with continuous action spaces (e.g., robotic control) or stochastic policies, where deterministic decisions might fail.
One key strength of REINFORCE is its simplicity and flexibility. It uses Monte Carlo sampling, meaning it collects full trajectories (sequences of states, actions, and rewards) from an episode to compute the gradient of the expected reward. This avoids the need for complex value function approximations. However, this approach also introduces high variance in gradient estimates, as small changes in actions can lead to large differences in outcomes. To mitigate this, developers often use techniques like reward baselines (subtracting a baseline value from rewards to reduce variance) or combine it with neural networks to parameterize the policy. For instance, a neural network could output action probabilities, and REINFORCE adjusts the network’s weights to favor actions that led to better outcomes. Despite its limitations, REINFORCE’s straightforward implementation makes it a starting point for understanding more advanced policy gradient methods like Actor-Critic algorithms.
REINFORCE has practical applications in scenarios where exploration and stochastic policies are critical. For example, in training a robot to walk, the algorithm might adjust the probabilities of motor actions based on whether a trial run (episode) resulted in the robot staying upright and moving forward. Another use case is simple game-playing agents, like navigating a grid world to reach a goal. Here, REINFORCE could learn to avoid pitfalls by increasing the likelihood of actions that historically led to success. While its sample inefficiency (requiring many episodes) and variance make it less suited for large-scale problems alone, it is often combined with modern techniques. For example, when paired with deep learning, REINFORCE forms the basis of algorithms like Proximal Policy Optimization (PPO), which scale to complex tasks like training AI for video games or simulation-based control systems.