DeepMind's DQN Reinforcement Learning Algorithm

DQN reinforcement was implemented by learning algorithm from the company
DeepMind, after which the Agent for the RiverRaid game in the OpenAI Gym environment was successfully trained using this algorithm.

Description

What are reinforcement learning algorithms?

Reinforcement learning (RL) is a field of machine learning concerned with how intelligent agents should act in an environment to maximize the notion of cumulative reward. Reinforcement learning is one of the three main paradigms of machine learning, along with supervised learning and unsupervised learning.

What is OpenAI Gym

OpenAI Gym is an open-source library that provides a simple setup and a toolkit that includes a wide range of simulated environments. These simulated environments range from very simple games (pong) to complex physics-based game engines. These environments allow you to quickly set up and train reinforcement learning algorithms.

The gym can also be used as a benchmark for reinforcement learning algorithms. Each environment in the OpenAI Gym toolkit contains a version that is easy to compare and reproduce when testing algorithms. These environments have episode-based settings for performing reinforcement learning, where the agent's experience is further divided into a series of episodes. This toolkit also provides a standard API for interacting with reinforcement learning environments. It is also compatible with other computational libraries such as TensorFlow. The initial release of OpenAI Gym consists of more than 1,000 environments for performing various categories of tasks.

Key Terminology

To understand OpenAI Gym and effectively use it for reinforcement learning, it is crucial to understand the key concepts.

Reinforcement Learning
Before diving into OpenAI Gym, it is important to understand the basics of reinforcement learning. In reinforcement learning, an agent performs a sequence of actions in an uncertain and often complex environment with the goal of maximizing a reward function. Essentially, it is an approach to making the right decisions in a game environment that maximizes rewards and minimizes penalties. Feedback from its own actions and experiences allows the agent to learn the most appropriate action through trial and error. Typically, reinforcement learning involves the following steps:

• Observation of the environment
• Formulation of a solution based on a specific strategy
• Actions
• Receipt of a reward or penalty
• Learning from experience to improve the strategy
• Iteration of the process until an optimal strategy is achieved
  For example, an unmanned vehicle must ensure the safety of passengers by following speed limits and traffic regulations. The agent (the imaginary driver) is motivated by rewards to ensure maximum passenger safety and will learn from its experiences in the environment. Rewards for correct actions and penalties for incorrect actions have been developed and defined. To ensure that the agent complies with speed limits and traffic regulations, the following points should be considered:

The agent should receive a positive reward for successfully adhering to the speed limit, as this is necessary for the safety of passengers. The agent should be penalized if they exceed the desired speed limit or are driving light. For example, the agent may receive a small negative reward for moving the vehicle before the countdown ends (the traffic light is still red).gent

Agent

In reinforcement learning, an agent is an entity that decides what actions to take based on rewards and punishments. To make a decision, the agent is allowed to use observations from the environment. It typically expects the current state to be provided by the environment and that this state will have a Markov property. It then processes this state using a policy function that decides what action to take. In OpenAI Gym, the term "agent" is an integral part of reinforcement learning. In short, an agent describes how to run a reinforcement learning algorithm in a gym environment. An agent can either contain the algorithm or provide the integration required for the algorithm and the OpenAI Gym environment.

The Environment

In the Gym, the environment is a simulation that represents the task or game that the agent is working on. When the agent performs an action in the environment, it receives observations from the environment that consist of a reward for that action. This reward informs the agent about how good or bad the action was. The observation tells the agent what its next state in the environment is. Thus, through trial and error, the agent tries to figure out the optimal behavior in the environment to best accomplish its task. One of the strengths of OpenAI Gym is the many pre-built environments designed to train reinforcement learning algorithms. You may want to browse the extensive list of environments available in the Gym toolkit.

Observations of the OpenAI Gym

If you want your reinforcement learning tasks to perform better than if you just performed random actions at each step, you should know what actions are available in the environment. These are:

Observation (object): An object related to the environment represents an observation of the environment. For example, pixel data from a camera.

Reward (floating): A reward is a scalar value provided to the agent in the form of feedback to control the learning process. The primary goal of the agent is to maximize the reward amount, and the reward signal indicates the agent's performance at any given stage. For example, in an Atari game, the reward signal may result in a +1 for every instance of increasing the score or a -1 for decreasing the score.

Done (logical): This is primarily used when the environment needs to be reset. In this process, most tasks are broken down into clearly defined objects, and True is an indicator of a completed episode. For example, in the Atari Pong game, if you lose the ball, the episode ends and you get "Done = True."

Info (dict) : This is useful for debugging purposes. For example, during the training phase, the model may have raw probabilities of when the environment's state last changed. However, you should note that the official agent evaluation cannot use this for training. This is a case of the "agent-environment loop." At each time step, the agent selects an action, and the environment returns an observation and a reward. This process begins with the call to reset(), which returns the initial observation.

If you are interested in this neural network and it can help you solve your business and other technical problems, please send us an email: info@ai4b.org