Openai policy gradient

1. How does policy gradient work?
2. Why is policy gradient better than Q-learning?
3. What is vanilla policy gradient?
4. Is Dqn a policy gradient method?

How does policy gradient work?

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.

Why is policy gradient better than Q-learning?

While Q-learning aims to predict the reward of a certain action taken in a certain state, policy gradients directly predict the action itself.

What is vanilla policy gradient?

The vanilla policy gradient algorithm uses an on-policy value function, which essentially means that the policy network is updated using experience collected from the latest interaction with the agent.

Is Dqn a policy gradient method?

Training. Unlike Q-Learning, the Policy Gradient algorithm is an on-policy algorithm — which means it learns only using state-action transitions made by the current active policy. Technically, this means there is not Experience Replay memory like in DQN.