  This paper provides the details of implementing two important policy gradient
methods to solve the inverted pendulum problem. These are namely the Deep
Deterministic Policy Gradient (DDPG) and the Proximal Policy Optimization (PPO)
algorithm. The problem is solved by using an actor-critic model where an
actor-network is used to learn the policy function and a critic network is to
evaluate the actor-network by learning to estimate the Q function. Apart from
briefly explaining the mathematics behind these two algorithms, the details of
python implementation are provided which helps in demystifying the underlying
complexity of the algorithm. In the process, the readers will be introduced to
OpenAI/Gym, Tensorflow 2.x and Keras utilities used for implementing the above
concepts.
