Reinforcement Learning is an area of Machine Learning that works in a problem space or environment where the agent or Reinforcement Learning algorithm, uses an understanding of the past test results and the potential paths through the environment to calculate a policy that solves for the goal of a maximized reward.

Given the variety of environments that RL can operate in, there are a range of approaches that the agent can take and are broadly categarized as six different approaches:
1. Associated RL - which combines traditional approaches of Machine Learning classification with the automated learning on random, or stochastically distributed results.
2. Deep RL - which leverages traditional deep neural networks but allows for flexibility and a lack of pre-defined layers and the state space.
3. Adversarial Deep RL - where end results are compared using vulnerabilities in state stored in Deep Neural Networks and attempts to find weaknesses in these models.
4. Fuzzy RL - which leverages a near natural language definition of if-then fuzzy rules which determine the calculation of the value of a result.
5. Inverse RL - which removes the common reward function evaluation part of the model and instead injects a human to observe and evalutate the results.
6. Safe RL - is an approach to defining policies that also takes into account the behavior of the agent in the envionment, such that those agent steps are reasonaable or respect safety constraints.