Q-learning Advantages and Disadvantages
Advantages:
- Long-term outcomes, which are exceedingly challenging to accomplish, are best achieved with this strategy.
- This learning paradigm closely resembles how people learn. Consequently, it is almost ideal.
- The model has the ability to fix mistakes made during training.
- Once a model has fixed a mistake, there is virtually little probability that it will happen again.
- It can produce the ideal model to address a certain issue.
Disadvantages:
- Drawback of using actual samples. Think about the situation of robot learning, for instance. The hardware for robots is typically quite expensive, subject to deterioration, and in need of meticulous upkeep. The expense of fixing a robot system is high.
- Instead of abandoning reinforcement learning altogether, we can combine it with other techniques to alleviate many of its difficulties. Deep learning and reinforcement learning are one common combo.
Q-Learning in Python
Reinforcement Learning is a paradigm of the Learning Process in which a learning agent learns, over time, to behave optimally in a certain environment by interacting continuously in the environment. The agent during its course of learning experiences various situations in the environment it is in. These are called states. The agent while being in that state may choose from a set of allowable actions which may fetch different rewards (or penalties). Over time, The learning agent learns to maximize these rewards to behave optimally at any given state it is in. Q-learning is a basic form of Reinforcement Learning that uses Q-values (also called action values) to iteratively improve the behavior of the learning agent.
This example helps us to better understand reinforcement learning.
Q-Learning