Advantages of Reinforcement Learning in Artificial Intelligence

Reinforcement Learning is a very powerful algorithm of artificial intelligence, science of decision making technology, learning about the optimal behavior of an environment to obtain maximum better results. This optimal behavior is learned with the help of environmental interactions & observations of how it responding as similar like to children exploring the world around them & learning the daily actions. In the absence of supervision, the learner must self independently discover the action sequences that maximizes the reward output. The quality of actions is measured by not just the immediate reward they return, but also the delayed reward they might fetch. As it can learn the actions that result in eventual success in an unseen environment without the help of a supervisor. In other words, you can say that Reinforcement Learning is the field of machine learning which involves learning without the human interaction as it has an agent that learns how to behave in an environment by performing actions & then learn based upon the outcome of these actions to achieve the required goal.

Some examples of Reinforcement Learning

Robotic Reinforcement Learning:

Robots with pre-programmed behavior are useful in structured environments, such as the assembly line of an automobile manufacturing plant, where the task is repetitive in nature. In the real world, where the response of the environment to the behavior of the robot is uncertain, pre-programming accurate actions is nearly impossible. In such scenarios, RL provides an efficient way to build general-purpose robots. It has been successfully applied to robotic path planning, where a robot must find a short, smooth, and navigable path between two locations, void of collisions and compatible with the dynamics of the robot.

Autonomous Driving:

An autonomous driving system must perform multiple perception and planning tasks in an uncertain environment. Some specific tasks where RL finds application include vehicle path planning and motion prediction. Vehicle path planning requires several low and high-level policies to make decisions over varying temporal and spatial scales. Motion prediction is the task of predicting the movement of pedestrians and other vehicles, to understand how the situation might develop based on the current state of the environment.

Benefits of Reinforcement Learning
Reinforcement learning is applicable to a wide range of complex problems that cannot be tackled with other machine learning algorithms. RL is closer to artificial general intelligence (AGI), as it possesses the ability to seek a long-term goal while exploring various possibilities autonomously. Some of the benefits of RL are mentioned below:

Focus on the problem:

Conventional machine learning algorithms are designed to excel at specific subtasks, without a notion of the big picture. RL, on the other hand, doesn’t divide the problem into subproblems; it directly works to maximize the long-term reward. It has an obvious purpose, understands the goal, and is capable of trading off short-term rewards for long-term benefits.

Works in dynamic environment:

RL algorithms are inherently adaptive and built to respond to changes in the environment. In RL, time matters and the experience that the agent collects is not independently and identically distributed, unlike conventional machine learning algorithms. Since the dimension of time is deeply buried in the mechanics of RL, the learning is inherently adaptive.

No need of separate data collection process:

In RL, training data is obtained via the direct interaction of the agent with the environment. Training data is the learning agent’s experience, not a separate collection of data that has to be fed to the algorithm. This significantly reduces the burden on the supervisor in charge of the training process.

Future of Reinforcement Learning

In recent years, significant progress has been made in the area of deep reinforcement learning. Deep reinforcement learning uses deep neural networks to model the value function (value-based) or the agent’s policy (policy-based) or both (actor-critic). Prior to the widespread success of deep neural networks, complex features had to be engineered to train an RL algorithm. This meant reduced learning capacity, limiting the scope of RL to simple environments. With deep learning, models can be built using millions of trainable weights, freeing the user from tedious feature engineering. Relevant features are generated automatically during the training process, allowing the agent to learn optimal policies in complex environments. In Reinforcement Learning, each task is learned by a separate RL agent, and these agents do not share knowledge. This makes learning complex behaviors, such as driving a car, inefficient and slow. Problems that share a common information source, have related underlying structure, and are interdependent can get a huge performance boost by allowing multiple agents to work together. Multiple agents can share the same representation of the system by training them simultaneously, allowing improvements in the performance of one agent to be leveraged by another. Asynchronous Advantage Actor-Critic is a latest development in this area, where related tasks are learned concurrently by multiple agents. This multi-task learning scenario is driving RL closer to AGI, where a meta-agent learns how to learn, making problem-solving more autonomous than ever before.

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