Implementing actor-critic RL algorithms
Actor-critic algorithms allow us to combine value-based and policy-based reinforcement learning – an all-in-one agent. While policy gradient methods directly search and optimize the policy in the policy space, leading to smoother learning curves and improvement guarantees, they tend to get stuck at the local maxima (for a long-term reward optimization objective). Value-based methods do not get stuck at local optimum values, but they lack convergence guarantees, and algorithms such as Q-learning tend to have high variance and are not very sample-efficient. Actor-critic methods combine the good qualities of both value-based and policy gradient-based algorithms. Actor-critic methods are also more sample-efficient. This recipe will make it easy for you to implement an actor-critic-based RL agent using TensorFlow 2.x. Upon completing this recipe, you will be able to train the actor-critic agent in any OpenAI Gym-compatible reinforcement learning...
