Understanding Reinforcement Learning and Deep Reinforcement Learning
Although reinforcement learning has been around for decades, it has been coupled with deep learning recently, which has yielded phenomenal results.
Based on the type of data available, machine learning is used to construct algorithms that can receive the input data and use statistical analysis to predict the output. It can make life and work simpler, freeing us from unnecessary tasks while working faster and more intelligently than an entire team. Moreover, there are distinct forms of machine learning. For instance, there’s reinforcement learning and deep reinforcement learning.
An area of machine learning is reinforcement learning. In a specific situation, the goal is to take appropriate action to enhance the reward. It is used to find the best action or direction in a given situation through different software and machines. It has the capability of solving many beneficial and real-life issues across a variety of fields, including game theory, control theory, organizational analysis, knowledge theory, simulation-based optimization techniques, multi-agent systems.
Benefits of Reinforcement learning:
- Reinforcement learning does not require broad datasets that are labeled.
- Reinforcement learning operates in real-time. When the machine tries new techniques on the fly, it incorporates experimentation to find better alternatives and optimization when the machine utilizes the better methods it has found so far.
- For tasks with objectives such as robots playing soccer or self-driving cars getting to their destinations or an algorithm that maximizes the return on investment on ad spending, reinforcement learning can be used.
- Learning for reinforcement is flexible. It doesn’t need additional training because it automatically tries to adapt on the fly to different environments.
The combination of reinforcement learning (RL) and deep learning on the other hand, is deep reinforcement learning. A broad variety of complex decision-making activities that were previously out of reach for a machine have been able to overcome this area of study.
Deep reinforcement learning is an artificial intelligence and machine learning category where smart machines are able to learn the same from their behavior as human beings. Although reinforcement learning has been around for decades, it has been coupled with deep learning far more recently, which has yielded phenomenal results. Large quantities of training data and substantial computing power are essential for deep learning. Data volumes have exploded over the last few years, while the cost of computing power has drastically decreased, allowing deep learning applications to explode.
Benefits of Deep Reinforcement learning:
- There is a diverse and broad dataset in the automotive sector that will power deep reinforcement learning. Already in use for autonomous vehicles, it will help change the industry’s manufacturing, servicing of vehicles, and overall automation.
- To be better investment managers than humans and to test trading strategies, artificial intelligence, namely deep reinforcement learning will be widely used.
- There is tremendous potential for deep reinforcement learning to transform healthcare, from assessing the best treatment strategies and diagnosis to clinical trials, new drug discovery, and automated treatment.
- Intelligent robots in warehouses are becoming more popular to sort goods and distribute them to the appropriate people. Deep reinforcement learning lets a robot acquire information based on whether it has succeeded or failed as it chooses a system to place in a container. It uses this information to work in the future more effectively.
According to Sudharsan Ravichandiran, author of the book, Hands-On Reinforcement Learning with Python, “Great things are happening around RL research each and every day. Deep Meta reinforcement learning will be the future of AI where we will be so close to achieving artificial general intelligence (AGI). Instead of creating different models to perform different tasks, with AGI, a single model can master a wide variety of tasks and mimics human intelligence.”