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Deep Reinforcement Learning: How It Works and Real World Examples


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Deep Reinforcement Learning is the combination of Reinforcement Learning and Deep Learning. This technology enables machines to solve a wide range of complex decision-making tasks. Hence, it opens up many new applications in industries such as healthcare, security and surveillance, robotics, smart grids, self-driving cars, and many more.

We will provide an introduction to deep reinforcement learning:

  • What is Reinforcement Learning?
  • Deep Learning with Reinforcement Learning
  • Applications of Deep Reinforcement Learning
  • Advantages and Challenges


What is Deep Reinforcement Learning?

Reinforcement Learning

Sequential decision-making is a core topic in the field of machine learning. It describes the task of deciding, from experience, the sequence of actions to perform in an uncertain environment in order to achieve specific goals. Hence, sequential decision-making tasks cover a wide range of possible applications.

Reinforcement Learning (RL) is a concept inspired by behavioral psychology (Sutton, 1984) to use a formal framework to solve decision-making tasks. The concept is that an AI agent is able to learn by interacting with its environment, similar to a biological agent. With the experience gathered, the AI agent should be able to optimize some objectives given in the form of cumulative rewards.

Deep Reinforcement Learning

In the past few years, Reinforcement Learning has become very popular due to its success in addressing challenging sequential decision-making problems.

Deep Reinforcement Learning is the combination of Reinforcement Learning with Deep Learning techniques to solve challenging sequential decision-making problems. The use of deep learning is most useful in problems with high-dimensional state space. This means, that with deep learning, Reinforcement Learning is able to solve more complicated tasks with lower prior knowledge because of its ability to learn different levels of abstractions from data.

To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. This makes it possible for machines to mimic some human problem-solving capabilities, even in high-dimensional space, which only a few years ago was difficult to conceive.

Applications of Deep Reinforcement Learning

Some prominent projects used deep Reinforcement Learning in games with results that are far beyond what is humanly possible. Deep RL techniques have demonstrated their ability to tackle a wide range of problems that were previously unsolved.

Deep RL has achieved human-level or superhuman performance for many two-player or even multi-player games. Such achievements with popular games are significant because they show the potential of deep Reinforcement Learning in a variety of complex and diverse tasks that are based on high-dimensional inputs. With games, we have good or even perfect simulators, and can easily generate unlimited data.

  • Atari 2600 games: Machines achieved superhuman-level performance in playing Atari games.
  • Go: Mastering the game of Go with deep neural networks.
  • Poker: AI is able to beat professional poker players in the game of heads-up no-limit Texas hold’em.
  • Quake III: An agent achieved human-level performance in a 3D multiplayer first-person video game, using only pixels and game points as input.
  • Dota 2: An AI agent learned to play Dota 2 by playing over 10,000 years of games against itself (OpenAI Five).
  • StarCraft II: An agent was able to learn how to play StarCraft II a 99\% win-rate, using only 1.08 hours on a single commercial machine.

Those achievements set the basis for the development of real-world deep reinforcement learning applications:

  • Robot control: Robotics is a classical application area for reinforcement learning. Robust adversarial reinforcement learning is applied as an agent operates in the presence of a destabilizing adversary that applies disturbance forces to the system. The machine is trained to learn an optimal destabilization policy. AI-powered robots have a wide range of applications, e.g. in manufacturing, supply chain automation, healthcare, and many more.
  • Self-driving cars: Deep Reinforcement Learning is prominently used with autonomous driving. Autonomous driving scenarios involve interacting agents and require negotiation and dynamic decision-making which suits Reinforcement Learning.
  • Healthcare: In the medical field,  Artificial Intelligence (AI) has enabled the development of advanced intelligent systems able to learn about clinical treatments, provide clinical decision support, and discover new medical knowledge from the huge amount of data collected. Reinforcement Learning enabled advances such as personalized medicine that is used to systematically optimize patient health care, in particular, for chronic conditions and cancers using individual patient information.
  • Other: In terms of applications, many areas are likely to be impacted by the possibilities brought by deep Reinforcement Learning, such as finance, business management, marketing, resource management, education, smart grids, transportation, science, engineering, or art. In fact, Deep RL systems are already in production environments. For example, Facebook uses Deep Reinforcement Learning for pushing notifications and for faster video loading with smart prefetching.

Challenges of Deep Reinforcement Learning

Multiple challenges arise in applying Deep Reinforcement Learning algorithms. In general, it is difficult to explore the environment efficiently or to generalize good behavior in a slightly different context. Therefore, multiple algorithms have been proposed for the Deep Reinforcement Learning framework, depending on a variety of settings of the sequential decision-making tasks.

Many challenges appear when moving from a simulated setting to solving real-world problems.

  • Limited freedom of the agent: In practice, even in the case where the task is well-defined (with explicit reward functions), a fundamental difficulty lies in the fact that it is often not possible to let the agent interact freely and sufficiently in the actual environment, due to safety, cost or time constraints.
  • Reality gap: There may situations occur, where the agent is not able to interact with the true environment but only with an inaccurate simulation of it. The reality gap describes the difference between the learning simulation and the effective real-world domain.
  • Limited observations: For some cases, the acquisition of new observations may not be possible anymore (e.g. the batch setting). Such scenarios occur for example in medical trials or tasks with dependence on weather conditions, or trading markets such as stock markets.

How those challenges can be addressed:

  • Simulation: For many cases, a solution is the development of a simulator that is as accurate as possible.
  • Algorithm Design: The design of the learning algorithms and their level of generalization has a great impact.
  • Transfer Learning: Transfer learning is a crucial technique to utilize external expertise from other tasks to benefit the learning process of the target task.

Reinforcement Learning and Computer Vision

Computer Vision is about how computers gain understanding from digital images and video streams. Computer Vision has been making rapid progress recently, and deep learning plays an important role.

Reinforcement learning is an effective tool for many computer vision problems, like image classification, object detection, face detection, captioning, and more. Reinforcement Learning is an important ingredient for interactive perception, where perception and interaction with the environment would be helpful to each other. This includes tasks like object segmentation, articulation model estimation, object dynamics learning, haptic property estimation, object recognition or categorization, multimodal object model learning, object pose estimation, grasp planning, and manipulation skill learning.

More topics of applying Deep Reinforcement Learning to computer vision tasks, such as

What’s next

In the future, we expect to see deep reinforcement algorithms going in the direction of meta-learning. Previous knowledge, for example in the form of pre-trained Deep Neural Networks, can be embedded to increase performance and reduce training time. Advances in transfer learning capabilities will allow machines to learn complex decision-making problems in simulations (gathering samples in a flexible way), and then use the learned skills in real-world environments.

Check out our guide about supervised learning vs. unsupervised learning, or explore another related topic:

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