Q-Learning

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

In Q learning

We replace iterative format of bellman equation

And use it in form of expectation

Equation for training of the RL agent

Q-Learning using Q Table

Deep Q-Learning

In Deep Q-learning, we use a neural network to approximate the Q-value function. The state is given as the input and the Q-value of all possible actions is generated as the output.

Deep Q Algorithm

Bellman Equation

In deep Q-learning, we use a neural network to approximate the Q-value function. The state is given as the input and the Q-value of all possible actions is generated as the output.

Deep Q-Learning using Neural Networks

Results

Result of Q-learning on MountainCar-v0 Gym Environment

Result of Q-learning on CartPole Gym Environment

Result of Deep Q-learning on Breakout Atari Gym Environment