Revolutionizing efficiency in reinforcement learning with new Bellman equation variants!
Reinforcement learning is a useful tool used in many areas. The Bellman equation is a key part of this, but it can be slow and not very efficient. To solve this, different versions of the Bellman equation have been created. These include methods like MonteCarlo, Temporal-Difference learning, Sarsa, Q-learning, Deep Q-Networks, and Hamilton-Jacobi-Bellman equation. Each method has its own level of complexity.
