This block will cover all the essential aspects of Reinforcement Learning used in various Machine Learning applications.
What is reinforcement learning?
We need technical assistance to simplify life, improve productivity and to make better business decisions. To achieve this goal, we need intelligent machines. While it is easy to write programs for simple tasks, we need a way to build machines that carry out complex tasks. To achieve this is to create machines that are capable of learning things by themselves. Reinforcement learning does this.
Reinforcement learning framework
You will learn some essential frameworks used for Reinforcement learning in this module.
Value-based methods – Q-learning
The ‘Q’ in Q-learning stands for quality. It is an off-policy reinforcement learning algorithm, which always tries to identify the best action to take provided the current state.
Exploration vs Exploitation
Here, you will discover all the key differences between Exploration and Exploitation used in Reinforcement learning.
SARSA stands for State-Action-Reward-State-Action. It is an on-policy reinforcement learning algorithm, which always tries to identify the best action to take from another state.
Q Learning vs SARSA
Here, you will discover all the key differences between Q Learning and SARSA used in Reinforcement learning.