With the recent advancements in artificial intelligence, we are seeing a rise in the ability of machines to make intelligent decisions on their own. A notable contribution to this process is credited to Reinforcement Learning (RL), a subfield of AI.
RL enables agents to take actions that maximize the reward over time by utilizing knowledge gained from previous experimentation. This approach has allowed RL to open up new doors in the form of autonomous systems across multiple domains.
This blog will be your complete guide to RL, its algorithms, real-world applications, and impact. So let’s dive right into it.
What is Reinforcement Learning?
Reinforcement Learning is a machine learning model where agents rely on trial and error learning by interaction with the environment. RL consists of three different components. These are;
- Agent: It represents the intelligent entity that performs an action i.e. interacting with the environment.
- Environment: It is the external system with which the agent interacts in order to achieve rewards.
- Action: The decisions taken by the agent in the environment to transition between different states in order to perform an interaction.
The agent interacts with the environment based on which it receives rewards or punishments. Rewards are positive outcomes that the agent works on maximizing through its interaction with the environment.
On the other hand, punishments are negative outcomes that should be minimized. Over time, the rewards and punishments help the agent to optimize its behavior in order to achieve desired outcomes. It can learn from past mistakes to minimize negative outcomes and maximize rewards.
Common Algorithms Of Reinforcement Learning:
The algorithms of RL are typically classified into two different categories, value-based algorithms and policy-based algorithms.
Value-based algorithms have a defined value for every state-action pair which tells the agent what outcome to expect from a specific action.
On the other hand, policy-based algorithms do not have a specific value defined. They work on a defined set of rules that can be updated in order to achieve higher rewards.
Based on these two categories, some common RL algorithms include;
Q-Learning:
Q-learning is a value-based algorithm that does not require explicit knowledge of the environment. It takes a value for each state-action pair known as the Q-value as the expected reward for that specific state-action pair. It then performs iterations based on each state-action pair with a variable Q-value to maximize reward.
SARSA (State Action Reward State Action):
SARSA algorithm is a policy-based algorithm that starts with a defined policy. Based on the defined set of rules of the policy, the model works to maximize rewards.
Deep Q-Networks:
Deep Q-Networks are a type of value-based algorithm that combines reinforcement learning with deep neural networks. This approach has very successful applications in robotics. Like Q-learning, this model also uses RL’s self-directed environment exploration approach to define a Q-value. This model uses its learning to base future rewards on past successful actions in order to maximize the rewards.
Policy Gradients:
Policy gradients are also policy-based algorithms that work with a defined set of policies to maximize rewards. This algorithm has successful applications in NLP. It learns complex action policies by iteratively updating the policy in the direction of higher rewards.
Applications of RL:
RL has a vast number of applications across multiple industries. Some common applications of RL include;
Gaming:
RL has been trained to play games at a superhuman level. Notable role of RL in the gaming industry includes Chess. The agents can be trained to analyze the moves of the competitor and perform an action that leads to a win.
Robotics:
The successful invention of human-like robots is highly dependent on RL. Using this model, robots have been able to perform complex tasks such as walking, grabbing objects, and navigating.
Autonomous Vehicles:
Self-driving cars are no longer a dream thanks to RL. Companies such as Wayve.ai and Google have successfully used RL to train cars to drive on their own.
NLP:
Natural Language Processing is an excellent way for machines to learn human language and sentiments. Companies like Google have been using RL to optimize their NLP models to respond better to human queries.
Healthcare:
RL has the ability to revolutionize the healthcare industry. Agents can be trained to diagnose illness based on symptoms. It can also be used to control medical devices and make them efficient.
These are just some applications of RL. It has vast applications across multiple industries and still tests are being performed to increase the scope of RL.
Impact Of RL:
RL has been revolutionizing multiple industries for some time now. It has allowed robots to perform complex tasks, improved the gaming industry, allowed the finance industry to make better investment decisions, and played its part in many other industries. As RL algorithms become more efficient and scalable, we can surely expect even more great advancements in the future.
Conclusion:
This concludes our guide on RL. We have explained everything about RL including what it is, its ground-breaking applications, and the impact of RL. We are hoping for more advancements in various industries as RL becomes more advanced. Till then, we can wait and hope for the best to come.
