# [MLS-C01] [Algorithms] Reinforcement Learning Algorithms

Posted by Oscaner on August 7, 2022

## Defined

• Machine learning technique which learns a strategy, called a policy, that optimizes an objective for an agent acting in an environment
• Agent performs an action, checks the state of the environment, and gets rewarded based on the resulting state of the environment
• Attempts to achieve the goal of maximizing the long-term reward that the agent receives resulting from its actions
• Good for solving problems where you want your model to make independent decisions.
• Markov Decision Process (MDP)
• Based on Markov Decision Processes (MDPs) models
• Works through a sequence of time steps with each step containing the following
• Environment: operating space of the RL model
• State: complete information describing the environment and relevant past steps
• Action: agent’s activity
• Reward: number that reflects the state resulting from the last action
• Observation: data about the state of the environment available at each step

## Use Cases

1. Robotics
2. Traffic light control
3. Predictive auto scaling
4. Tuning parameters of a web system
5. Optimizing chemical reactions
6. Personalized recommendations
7. Gaming
8. Deep learning: “see an environment” and learn how to interact with it

## SageMaker Algorithms

### Deep Learning Framework

• Reinforcement Learning in TensorFlow and Apache MXNet
• Uses a Reinforcement Learning toolkit
• Manage the agents interaction with the environment
• SageMaker supports Intel Coach and Ray RLlib toolkits
• Uses an environment
• Custom environment
• Open source environments: EnergyPlus, RoboSchool
• Commercial environments: MATLAB, Simulink
• Important Hyperparameters
1. learning_rate: how fast the model learns
2. discount_factor: take action with short-term or long-term rewards
3. entropy: degree of uncertainty; exploit what’s already known versus thorough exploration of the environment