Reinforcement learning algorithms concept

Let's create a simplistic model for reinforcement learning with an introduction of the basic terminologies: 

At each step and time (t), the agent:

• Executes action a_t
• Receives observation o_t
• Receives a reward r_t

At each step and time (t), the environment:

• Receives action a_t
• Generates observation ot+1
• Generates scalar reward r_t+1

The environment is considered to be non-deterministic (action a_t based on o_t will receive reward r_t and the same action in the same state may result in different rewards). 

The agent (intelligent machine) is connected to the environmental context with its observation and action. The agent perceives the environment in a unique-to-itself manner and decides the action based on some of the popular and evolving techniques. At each step in time, the agent receives signals that represent the state of the environment.

The agent responds with an action that is one among several possible options at that point in time. The action generates an output...