memetic q-learning

Memetic Q-learning is a type of reinforcement learning algorithm that combines the traditional Q-learning approach with evolutionary algorithms. The idea behind memetic Q-learning is to use genetic algorithms to optimize the Q-values of the states and actions in a reinforcement learning problem. This approach allows the algorithm to learn faster and more efficiently than traditional Q-learning by exploiting the strengths of both approaches.

In memetic Q-learning, the algorithm starts with a population of Q-tables, which represent the value of each state-action pair in the problem. The population is then evolved using genetic algorithms, which select the best Q-tables and use them to generate new ones through mutation and crossover operations. The Q-values of the new tables are then updated using the traditional Q-learning update rule.

Memetic Q-learning has been shown to be effective in several applications, including robot control, game playing, and portfolio optimization. However, it can be computationally expensive due to the need to maintain a population of Q-tables and perform evolutionary operations on them.