Reinforcement Learning: Reinforcement Learning, agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones.
For example, consider teaching a dog a new trick: you cannot tell it what to do, but you can reward/punish it if it does the right/wrong thing. It has to figure out what it did that made it get the reward/punishment, which is known as the credit assignment problem.
Reinforcement Learning can be further divided into Positive and Negative RL Algorithms:
- Positive: Positive Reinforcement is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior. In other words, it has a positive effect on behavior.
- Negative: Negative Reinforcement is defined as strengthening of a behavior because a negative condition is stopped or avoided. It increases behavior.
Some popular examples of Reinforcement learning algorithms are:
- In Gaming frontier, AlphaGo Zero.
- Reinforcement Learning in news recommendation.
- Discord Bots to learn and maintain a user’s behavior.