sakshee5/reinforcement-learning-with-value-based-algorithms

Getting-started-with-Reinforcement-Learning

This is an introduction to Reinforcement Learning using Q-Learning and Deep Q-Learning.

1. Q Learning

Q Learning is demonstrated using OpenAI's Frozen Lake Environment. You can read more about the environment Here.

RL concepts used:

1. Markov Decision Process and it's components i.e. The Agent, Environment, State, Action, Reward
2. Expected Discounted Return (Discount Rate)
3. Epsilon-Greedy Strategy
4. Bellman Optimality Equation (Optimal policy, Optimal Q function, Learning Rate)

2. Deep Q-Learning

Deep Q Network is demonstrated using OpenAI's Cart Pole Environment. You can read more about the environment Here.

RL concepts used beside the ones stated above:

1. Artificial Neural Network Architecture
2. Experience Replay
3. Training and Target Networks

P.S. Both codes are well commented/documented for ease of understanding.