Reinforcement Learning.txt

Reinforcement Learning
Reinforcement Learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment.
It is a part of the deep learning method that helps you to maximize some portion of the cumulative reward.
Discussing below the main elements of RL
Agent — 
the learner and the decision maker.
Environment — 
where the agent learns and decides what actions to perform.
Action — 
a set of actions which the agent can perform.
State — 
the state of the agent in the environment.
Reward — 
for each action selected by the agent the environment provides a reward. Usually a scalar value.
Policy — 
the decision-making function (control strategy) of the agent, which represents a mapping from situations to actions.
Value function — 
mapping from states to real numbers, where the value of a state represents the long-term reward achieved starting from that state, and executing a particular policy.
Function approximator — 
refers to the problem of inducing a function from training examples. Standard approximators include decision trees, neural networks, and nearest-neighbor methods
Model — 
The agent’s view of the environment, which maps state-action pairs to probability distributions over states. Note that not every reinforcement learning agent uses a model of its environment