Hands-On Intelligent Agents with OpenAI Gym

The deep n-step advantage actor-critic algorithm

In our deep Q-learner-based intelligent agent implementation, we used a deep neural network as the function approximator to represent the action-value function. The agent then used the action-value function to come up with a policy based on the value function. In particular, we used the -greedy algorithm in our implementation. So, we understand that ultimately the agent has to know what actions are good to take given an observation/state. Instead of parametrizing or approximating a state/action action function and then deriving a policy based on that function, can we not parametrize the policy ...