What is a policy gradient method?

A policy gradient method is a type of reinforcement learning algorithm that directly optimizes the policy—the strategy an agent uses to select actions—by adjusting its parameters to maximize expected rewards. Unlike value-based methods, which first estimate the value of states or actions and then derive a policy, policy gradients work by tweaking the policy itself through gradient ascent. The policy is typically represented as a parameterized function (e.g., a neural network) that outputs probabilities of taking actions in a given state. For example, in a game-playing agent, the policy might decide the probability of moving left or right based on the current screen pixels.

The core idea revolves around calculating the gradient of the expected reward with respect to the policy parameters. This gradient tells the algorithm how to adjust the parameters to increase the likelihood of high-reward actions. During training, the agent interacts with the environment, collects trajectories (sequences of states, actions, and rewards), and uses these to estimate the gradient. For instance, if an action in a specific state leads to a high reward, the policy is updated to make that action more probable in similar future states. This process often relies on the “policy gradient theorem,” which provides a mathematical framework to compute gradients efficiently, even when the environment’s dynamics are unknown. Techniques like Monte Carlo sampling or actor-critic methods are commonly used to reduce variance in gradient estimates, making training more stable.

A well-known example of a policy gradient method is REINFORCE, a simple algorithm that multiplies the reward of an entire episode by the gradient of the log-probability of the actions taken. More advanced variants, like Proximal Policy Optimization (PPO), introduce constraints or surrogate objectives to prevent large policy updates that could destabilize training. Policy gradients are particularly useful in environments with continuous action spaces (e.g., robotics control, where actions might represent motor torques) because they can directly model stochastic policies. However, they often require careful tuning of hyperparameters, such as learning rates and discount factors, and may struggle with high variance in reward signals. Despite these challenges, their flexibility and direct optimization approach make them a popular choice in complex reinforcement learning tasks.