What is the policy gradient method in reinforcement learning?

The policy gradient method is a reinforcement learning technique that directly optimizes a policy, which is a function mapping states to actions. Instead of learning a value function (like Q-learning) and deriving a policy from it, policy gradient methods adjust the policy parameters to maximize the expected cumulative reward. The core idea is to compute the gradient of the expected reward with respect to the policy parameters and update the parameters in the direction that increases this reward. This approach is particularly useful for environments with continuous action spaces or complex policies that are hard to represent with value-based methods.

A key example of a policy gradient algorithm is REINFORCE. In REINFORCE, the policy is typically a neural network that outputs probabilities for each action. After completing an episode, the algorithm calculates the gradient by multiplying the log probability of each action taken by the cumulative reward (discounted or undiscounted) obtained in the episode. For instance, in a game where an agent moves left or right, the network might output a 70% probability for “left” and 30% for “right.” If the episode results in a high reward, the gradients for the actions taken are scaled by that reward, making those actions more likely in similar states. Another example is the Actor-Critic method, which combines policy gradients with a value function (the “critic”) to reduce variance in gradient estimates by using the critic to baseline the rewards.

Policy gradient methods offer flexibility, such as handling stochastic policies and continuous actions, but they face challenges like high variance in gradient estimates and sample inefficiency. For example, training a robot arm to grasp objects requires precise control over joint angles (continuous actions), which policy gradients can model directly. To address high variance, techniques like advantage estimation (subtracting a baseline reward) or trust region methods (e.g., Proximal Policy Optimization) are used. These methods constrain policy updates to avoid drastic changes that could destabilize training. While slower to converge than value-based methods in some cases, their direct optimization approach makes them a staple for complex reinforcement learning tasks.