Terms Used in Reinforcement Learning
An entity that can perceive/explore the environment and act upon it.
A situation in which an agent is present or surrounded by. In RL, we assume the stochastic environment, which means it is random in nature.
Actions are the moves taken by an agent within the environment.
State is a situation returned by the environment after each action taken by the agent.
A feedback returned to the agent from the environment to evaluate the action of the agent.
Policy is a strategy applied by the agent for the next action based on the current state.
It is expected long-term retuned with the discount factor and opposite to the short-term reward.
It is mostly similar to the value, but it takes one additional parameter as a current action (a).
Key Features of Reinforcement Learning
In RL, the agent is not instructed about the environment and what actions need to be taken.
It is based on the hit and trial process.
The agent takes the next action and changes states according to the feedback of the previous action.
The agent may get a delayed reward.
The environment is stochastic, and the agent needs to explore it to reach to get the maximum positive rewards.
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