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Improving Decision Making with Artificial Neural Networks

  • July 12, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Introduction

We'll talk about make decisions within artificial neural networks of neurons (ANNs) in this blog.

Because many of the links between inputs and outcomes in everyday life are non-linear and complex, it is crucial that ANNs be able to learn and simulate nonlinear and complex interactions. These machines learning neural networks are what we employ for decision-making. The autonomous vehicle, or self-driving automobile, is one of the finest uses of machine learning neural networks.

The ability to make decisions is essential in self-driving cars.

Self-driving automobiles use sensors to perceive their environment. But how can they make sense of all this information?

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The ability to take in and analyse road data while driving, encompassing everything from road signs to pedestrian to neighbouring automobiles, is known in the business as perception. AI enables driverless cars to observe and react to their surroundings in real-time, which enables them to move safely.

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They employ a collection of techniques called deep neural networks (DNNs) to do this. Instead of needing to follow a humanly established set of rules, such "Stop if you see red," DNNs allow cars to learn how to navigate the environment on their own using sensor data.

These mathematical models, which learn through experience, were inspired by the human brain. If given several photographs of stop signs in various scenarios, a DNN may automatically learn to recognise stop signs. A single algorithm, however, is unable to do the job by itself. A comprehensive collection of DNNs, each devoted to a certain task, is necessary for safe autonomous driving. They need a flexible and precise system in an uncertain world. It must take into consideration the imperfection of some sensor data and the unpredictable nature of human decision-making when driving. These things are difficult to put a number on. Even if we could measure them, we couldn't predict them with any degree of accuracy.

These networks are capable of reading signs, locating intersections, and recognising driving patterns, among many other functions. Additionally, they are redundant and have overlapping roles to lower the possibility of failure.

No set amount of DNNs is required for autonomous driving. And because new abilities are always being developed, the list is constantly growing and evolving. In order to operate the car, the signals produced by the several DNNs must be evaluated in real-time.

When it comes to making judgements, deep reinforcement learning (DRL) can be used. The Markov decision process (MDP), a decision-making method, is the foundation of DRL. Typically, an MDP is used to predict future conduct of road users. It's crucial to keep in mind that things might become quite confusing as the number of items, especially moving ones, increases. As a result, the self-driving car will increasingly have a greater range of motions available to it. To overcome the difficulty of figuring out the best course of action for itself, the deep learning model is tweaked via Bayesian optimisation. The framework, which combines Bayesian optimisation with a hidden Markov model, is sometimes used to guide judgements.

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Markov decision process (MDP)

Take a moment to find the major city closest to you. What would you do if you were to go there? Do you go by car, bus or train? Maybe ride a bike or buy a plane ticket?

Include probabilities in the decision-making process when making this choice. Perhaps there is a 70% chance that a rain or car accident will occur, causing traffic jams. If your bike's tires are old, it can get worse-it's certainly a big probability factor.

In contrast, there are predictable expenses like the price of petrol and airline tickets, as well as deterministic benefits like much shorter flying durations.

Decision-making frequently involves issues where agents must weigh probabilistic and deterministic benefits and costs. These optimisation issues are modelled using Markov decision processes, which may also be employed for more difficult reinforcement learning tasks.

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Key components of MDP are

  • Agent: We are teaching an RL agent to make accurate judgments (for eg: a Robot that is being trained to move around a house without crashing).
  • Environment: The location in which an agent interacts is referred to as the agent's environment (for example, the home where the Robot resides). The agent only has control over its own actions; it has no impact on the surroundings. For instance, the robot cannot change where a table is stored in the house, but it may walk around it to avoid colliding.State: The agent's present state is defined by the state (for eg: it can be the exact position of the Robot in the house, the alignment of its two legs, or its current posture; it depends on how you address the problem).
  • Action: The choice the agent made in the current time step, such as whether to move its right or left leg, raise an arm, lift something, or turn to the right or left. We are aware of the options the agent has in advance.
  • Policy: A policy is an intellectual process that goes into deciding on a course of action. It is a probability distribution attributed to the collection of activities in practice. Highly rewarding activities are more likely to occur, and vice versa.

Imagine a game of dice to explain the Markov decision process.

Each round can be continued or ended. If you quit, you will receive 5 and the game will end. If you continue, you will earn Rs 3 and roll a 6-sided die. If the dice roll 1 or 2, the game ends. Otherwise, the game will continue in the next round. There is a clear compromise here. One can trade the deterministic Rs 2 win for a chance to roll the dice and move on to the next round.

We must first specify a few terms before we can build an MDP that simulates this game. The agent's (the decision maker's) status is the status that they can adopt. Agents are either in-play or out-of-play in Claps. The agent might choose a movement for the activity. Agents can be moved across states with certain fines or benefits. The transition probability is an indicator of how likely it is that action a will led to the state's (s prime). The probability of ending with s' given the present state s and the action an is returned by these, which are frequently referred to as the function P (s, a, s'). For instance, P has a 2/6 (1/3) probability of losing the dice (s = play the game, a = opt to continue playing, s' = do not play the game). This must be completed. Your award will be determined by your activities. The prize for finishing the game is Rs 5, while the prize for continuing it is Rs 3. The compensation is "total" and optimised. The formal definition of the Markov decision process is m = (S, A, P, R, gamma). where S stands for a collection of all states. A stand for the range of potential responses. The transition probability is denoted by P. R stands for the prize. The discount factor is also referred to as gamma (more on this later). Finding a policy, commonly referred to as Pi, that offers the best long-term returns is the objective of MDP m. Only each state's distribution of activities is mapped by the policy. The agent must take action with a specific probability for each state. The policy can also be deterministic, in which case the agent acts in states.Markov The decision process is shown in the following graph. The agent traverses the two states of the graph, making decisions and tracking probabilities.

It is important to mention Markov properties that apply not only to the Markov decision process, but also to Markov-related (such as Markov chains).

It is stated that the following states can be determined only by the current state. No "memory" is needed. This applies to how the agent goes through the Markov decision process, but keep in mind that the optimization method uses pre-learning to fine-tune the policy. This is not a Markov property violation that applies only to MDP traversals.

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The Bellman equation

The Markov decision process relies heavily on the Bellman equation. Although there are many complicated forms of the Bellman equation, most of them may be simplified to the following form:

The highest reward an agent may obtain for choosing the optimal course of action in the present state and all ensuing states is determined by the Bellman equation. The value of the current state is defined recursively as the sum of the value of the current state plus the value of the next state.

Dynamic programming computes new values depending on previously computed values that are stored in a lattice structure. Not only the Markov decision process, but also many other recursive issues, may be efficiently solved using it.

The process of learning Q values in a setting that closely matches the Markov decision process is known as Q-learning. The optimal methods are learned through repeated exploration of the environment, which makes agents useful for situations where some probabilities, rewards, and penalties are not fully understood.

I hope it was fun learning more about these subjects with you.

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