TensorFlow Machine Learning Projects

Components of Reinforcement Learning

In any RL formalization, we talk in terms of a state space and an action space. Action space is a set of finite numbers of actions that can be taken by the agent, represented by A. State space is a finite set of states that the environment can be in, represented by S.

The goal of the agent is to learn a policy, denoted by . A policy can be deterministic or stochastic. A policy basically represents the model, using which the agent to  select the best action to take. Thus, the policy maps the rewards and observations received from the environment to actions.

When an agent follows a policy, it results in a ...