The policy gradient method in reinforcement learning is an approach where the agent learns a policy directly, rather than learning a value function. The policy is represented by a probability distribution over actions given a state, and the goal is to find the parameters of this distribution that maximize the expected reward.
In policy gradient methods, the policy is parameterized using a neural network. The agent takes actions according to the policy, and the gradient of the expected return with respect to the policy parameters is calculated using gradient ascent. The gradient is used to update the parameters, improving the policy over time. A key aspect of policy gradients is that they can be used in environments with continuous action spaces, unlike Q-learning, which typically works with discrete actions.
One common algorithm that uses policy gradients is the REINFORCE algorithm, which performs Monte Carlo updates to the policy based on the cumulative rewards from an episode. Policy gradient methods are well-suited for environments like robotics, where the action space can be large and continuous.