Vanilla Policy Gradient(VPG)-RL

Reinforcement learning (RL) is the branch of machine learning that is concerned with making sequences of decisions. It considers an agent situated in an environment: each timestep, the agent takes an action, and it receives an observation and reward. An RL algorithm seeks to maximize the agent's total reward, given a previously unknown environment, through a trial-and-error learning process. The key idea of policy gradients is to push up the probabilities of actions that lead to higher return, and push down the probabilities of actions that lead to lower return, until you arrive at the optimal policy. 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.