Reinforcement Learning: Problems and Real-life applications
Reinforcement learning is a method for teaching an autonomous agent that observes and acts in its surroundings to pick the best actions to achieve its objectives. In this blog, we’ll discuss problems in reinforcement learning and its real-life applications.
A few of the general problems of reinforcement learning involve:
- Learning to regulate sequential processes – manufacturing optimization challenges in which the reward is the value of the commodities produced minus the expenses associated.
- Specific settings: actions are deterministic or non-deterministic, the agent does or does not have prior knowledge of the effects of its actions on the environment.
- Sequential scheduling – choosing which taxis to send for passengers in a big city where the reward is a function of passenger wait time and total fuel costs of the taxi fleet.
Reinforcement Learning Problems:
- Delayed reward
- Exploration
- Partially observable states
- Life-long learning
1. Delayed-Reward:
The agent’s job is to figure out a target function n that translates from the current state s to the best action a = (s).
In other words, each training example for a target function would be a pair of the type (s, n(s)). However, training material is not available in this manner in reinforcement learning.
Instead, when the agent performs its sequence of behaviors, the trainer just offers a series of quick reward values. As a result, the agent must solve the challenge of assigning temporal credit: choosing which of the activities in its sequence should be credited with creating the final rewards.
2. Exploration:
The action sequence chosen by the agent in reinforcement learning determines the distribution of training instances.
This begs the question of which technique of experimenting delivers the most effective learning.
The learner must choose between exploring novel states and actions (gathering new knowledge) and exploitation of states and actions it has previously learned, which will provide a higher reward (to maximize its cumulative reward).
3. Partially observable states:
Although it is common to assume that the agent’s sensors can observe the whole state of the environment at each time step, sensors often only offer partial information in real-world scenarios.
A robot with a forward-facing camera, for example, cannot see what is behind it. In such instances, the agent may need to incorporate earlier observations as well as current sensor data when making decisions, and the ideal policy may be one that prioritizes activities that increase the environment’s observability.
4. Life-long learning:
Robot learning, unlike isolated function approximation tasks, frequently necessitates the robot learning numerous related tasks in the same environment while utilizing the same sensors.
A mobile robot, for example, may need to learn how to dock on its battery charger, travel through tight passageways, and pick up output from laser printers.
When learning new tasks, this option increases the possibility of applying previously acquired experience or knowledge to minimize sample complexity.
Applications:
In real life, the agent investigates the surroundings without the need for human interaction. It is the most widely used learning algorithm in Artificial Intelligence.
However, there are some situations in which it should not be utilized, such as when there is sufficient data to answer the problem and other machine learning techniques may be employed more effectively.
The fundamental problem with the RL algorithm is that certain settings, such as delayed feedback, might alter the learning pace.
Deep Reinforcement Learning:
It has been proposed in a number of articles for autonomous driving. There are several factors to consider with self-driving automobiles, including speed limitations in various locations, drivable zones, and avoiding crashes, to name a few.
Trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways are some of the autonomous driving activities where reinforcement learning might be used.
Learning automated parking regulations, for example, can help with parking. Q-Learning may be used to change lanes, and overtaking can be done by learning an overtaking strategy while avoiding collisions and keeping a constant speed thereafter.
To manage the throttle and direction, a reinforcement learning model is used.
Trading and financeuse of reinforcement learning:
Forecasting future sales and stock prices may both be done with supervised time series models. These models, on the other hand, do not determine what to do at a given stock price.
This is where Reinforcement Learning comes in (RL). An RL agent can select whether to keep, purchase, or sell a task. To guarantee that the RL model is working properly, it is assessed using market benchmark criteria.
Unlike prior techniques, which required analysts to make each and every choice, automation ensures uniformity throughout the process. IBM, for example, has developed a powerful reinforcement learning-based platform that can execute financial transactions.
Every financial transaction’s loss or profit is used to calculate the reward function.
- Text summarization, question answering, and machine translation are just a few of the applications of RL in NLP.
- Game Playing: RL may be utilized in games such as tic-tac-toe, chess, and other similar games.
- Chemistry: RL may be applied to chemical reaction optimization.
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