Policy Gradient Algorithms

Policy gradient algorithms work by parameterizing the policy as a differentiable function, such as a neural network, and interacting with the environment to collect transitions and rewards. The policy is then updated based on the gradient of the expected return with respect to the policy parameters, using an optimization algorithm such as stochastic gradient descent.