Terms Used in Reinforcement Learning

Terms Used in Reinforcement Learning

  • Agent()
    An entity that can perceive/explore the environment and act upon it.

  • Environment()
    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.

  • Action()
    Actions are the moves taken by an agent within the environment.

  • State()
    State is a situation returned by the environment after each action taken by the agent.

  • Reward()
    A feedback returned to the agent from the environment to evaluate the action of the agent.

  • Policy()
    Policy is a strategy applied by the agent for the next action based on the current state.

  • Value()
    It is expected long-term retuned with the discount factor and opposite to the short-term reward.

  • Q-value()
    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.