Introduction to Reinforcement Learning

Greetings, readers! I hope you're all doing well.

This blog would be an introduction to reinforcement learning and the RL Coach by Intel AI. But, before we jump into reinforcement learning, let's go back to the beginning. So, it all started with Machine Learning. Machine learning, as the name implies, allows machines to learn without having to be explicitly programmed. Machine Learning is roughly divided into three categories:

- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning

The machine learning job of learning a function that translates input to an output based on example input-output pairs is known as supervised learning (SL). It uses labelled training data and a collection of training examples to infer a function. A supervised learning algorithm examines the training data and generates an inferred function that can be applied to fresh cases.

Unsupervised learning is a sort of algorithm that uses un-labelled data to learn patterns. The goal is that the machine will be pushed to develop a compact internal picture of its surroundings through imitation, and then produce inventive material from it.

Let's assume you have a pet, which can be any pet. Assume it's a dog. When we bring a dog into our home, we teach it how to respond to specific commands. When we say "sit," it obeys our direction and sits down. But what motivates the dog to pay attention to us? It is the prize it receives after acting in accordance with the provided command. We treat the dog with its favorite biscuits if he obeys our command. As a result, one of the main motivations for the dog's learning is the reward. Reinforcement Learning operates in the same way.

"Reinforcement learning is the process of taking appropriate action in order to maximize reward in a given situation."

Reinforcement Learning is made up of four main components:

• Agent: The entity tasked with making judgments.
• Actions: An entity's actions are referred to as "actions."
• Environment: The context in which an agent performs an activity is referred to as the environment. It's governed by a set of laws that dictate how the environment reacts to each activity.
• State: After each action done by the agent, the environment returns a state to the agent.
• Reward: A technique that informs the agent of his or her success rate.

Now after we are clear with the main components, let’s consider an example. The scenario is as follows: we have an agent and a reward, but there are numerous obstacles in the way. The agent's job is to find the most efficient route to the reward. The difficulty is better explained in the following problem.

The Valorant character Viper, Spike, and Swamp Grenades are shown in the above illustration. The Viper's mission is to obtain the spike which is the reward while avoiding the Swamp Grenades obstacles. Viper learns by attempting all feasible courses and then selecting the one that provides the best reward with the fewest obstacles. Each correct step will award the viper, while each incorrect step will deduct the payout. When it reaches the final reward, which is the spike, the entire reward will be determined.

The most important aspects of reinforcement learning -

1. The input should be a starting state for the model to work from.
2. There are numerous possible outputs, just as there are numerous solutions to a given problem.
3. The model will return a state based on the input, and the user will select whether to reward or punish the model based on its output.
4. The model is always evolving.
5. The optimal solution is determined by the highest possible payment.

Application of RL

In the subject of robotics, RL has a fantastic applicability. When no success metric is offered, a robot must have the intelligence to perform unfamiliar tasks. It must investigate all alternatives in order to achieve its predetermined purpose. A robot here is an agent, according to standard Reinforcement Learning terminology. Its movement is the action. Another amazing application of RL can be seen in Autonomous Driving. In an uncertain environment, an autonomous driving system must execute many perception and planning tasks. Vehicle path planning and motion prediction are two examples of activities where RL can be used. To make judgments over diverse temporal and spatial scales, vehicle path planning necessitates a number of low and high-level policies. The challenge of forecasting the movement of pedestrians and other vehicles in order to comprehend how the situation may develop depending on the existing condition of the environment is known as motion prediction.

Introduction to RL Coach by Intel

Coach is a Python reinforcement learning framework that implements a number of cutting-edge techniques. It exposes a set of basic APIs for experimenting with novel RL algorithms, as well as allowing for the quick integration of new contexts to solve. Everything is mentioned here on the GitHub repository: Intel RL Coach

Even so, I'll go through the important points in this blog so you don't miss anything important during the installation and environment setup. As previously noted, RL Coach has only been tested on Ubuntu and Python 3.

• It is also recommended to install coach in a virtual environment:

  sudo -E pip3 install virtualenv
  virtualenv -p pythin3 coach_env
  . coach_env/bin/activate
  

• Finally, install Reinforcement Learning Coach using pip:

  pip3 install rl_coach
  

  
  

  Framework Support

• Built with the Intel-optimized version of TensorFlow* to enable efficient training of RL agents on multi-core CPUs.
• As of release 0.11.0 Reinforcement Learning Coach now supports Apache MXNet*
• Additionally, trained models can now be exported using ONNX to be used in deep learning frameworks not currently supported by Coach.*

Algorithms Support

Supported Simulation Environments

• OpenAl Gym
  • Mujoco, Atari, Roboschool, and PyBullet
  • Any environment that implements Gym's API
• DeepMind Control Suite
• ViZDoom
• CARLA Urban Driving Simulator
• StarCraft II

Backends And Datastores

• Single-node
• Kubernetes* (Orchestration)
  • Redis (Memory Backend)
  • S3 and NFS (Data Storage)
  • Docker containers
• Single-node

This brings us to the conclusion of this introductory blog post. I strongly encourage you to visit the Intel RL Coach repository (link provided above) and read the README thoroughly before giving it a try. Please share your opinions or questions in the comments area below, and until then, keep safe!

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