POLICY GRADIENT METHODS

Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. They do not suffer from many of the problems that have been marring traditional reinforcement learning approaches such as the lack of guarantees of a value function, the intractability problem resulting from uncertain state information and the complexity arising from continuous states & actions.

VISIT THE FOLLOWING RESOURCES TO LEARN MORE:

-Policy Gradient with PyTorch -Policy Gradients and Advantage Estimation (Foundations of Deep RL Series) -An introduction to Policy Gradient methods -Policy Gradients - Coursera