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Multi-armed bandits (MAB) is a unique Reinforcement Learning (RL) issue with a wide range of applications and a growing following. By ignoring the state and attempting to strike a balance between exploration and exploitation, multi-armed bandits expand RL.

Reinforcement learning is a machine learning training strategy that rewards desirable behaviours while penalising undesirable ones. A reinforcement learning agent can perceive and comprehend its surroundings, act, and learn through trial and error in general.

Your cat is an agent that is exposed to the environment, which is an example of reinforcement learning. The most notable feature of this system is that there is no supervisor involved; instead, a genuine number or incentive signal is used. There are two types of reinforcement learning:

  • Positive
  • Negative.

Reinforcement comes in four forms:

  • Positive reinforcement
  • negative reinforcement
  • extinction
  • punishment.
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Reinforcement can be used to teach new abilities, replace an interfering behaviour with a replacement behaviour, promote suitable behaviours, or increase on-task behaviour. Reinforcement may appear to be a straightforward method that many teachers employ, but it is frequently underutilised.

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