In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones.
In this article, we’ll look at some of the real-world applications of reinforcement learning.
Applications in self-driving cars
Various papers have proposed Deep Reinforcement Learning for autonomous driving. In self-driving cars, there are various aspects to consider, such as speed limits at various places, drivable zones, avoiding collisions — just to mention a few.
Some of the autonomous driving tasks where reinforcement learning could be applied include trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways.
For example, parking can be achieved by learning automatic parking policies. Lane changing can be achieved using Q-Learning while overtaking can be implemented by learning an overtaking policy while avoiding collision and maintaining a steady speed thereafter.
AWS DeepRacer is an autonomous racing car that has been designed to test out RL in a physical track. It uses cameras to visualize the runway and a reinforcement learning model to control the throttle and direction.
Wayve.ai has successfully applied reinforcement learning to training a car on how to drive in a day. They used a deep reinforcement learning algorithm to tackle the lane following task. Their network architecture was a deep network with 4 convolutional layers and 3 fully connected layers. The example below shows the lane following task. The image in the middle represents the driver’s perspective.
Industry automation with Reinforcement Learning
In industry reinforcement, learning-based robots are used to perform various tasks. Apart from the fact that these robots are more efficient than human beings, they can also perform tasks that would be dangerous for people.
A great example is the use of AI agents by Deepmind to cool Google Data Centers. This led to a 40% reduction in energy spending. The centers are now fully controlled with the AI system without the need for human intervention. There is obviously still supervision from data center experts. The system works in the following way:
- Taking snapshots of data from the data centers every five minutes and feeding this to deep neural networks
- It then predicts how different combinations will affect future energy consumptions
- Identifying actions that will lead to minimal power consumption while maintaining a set standard of safety criteria
- Sending and implement these actions at the data center
The actions are verified by the local control system.
Reinforcement Learning applications in trading and finance
Supervisedtime series models can be used for predicting future sales as well as predicting stock prices. However, these models don’t determine the action to take at a particular stock price. Enter Reinforcement Learning (RL). An RL agent can decide on such a task; whether to hold, buy, or sell. The RL model is evaluated using market benchmark standards in order to ensure that it’s performing optimally.
This automation brings consistency into the process, unlike previous methods where analysts would have to make every single decision. IBM for example has a sophisticated reinforcement learning based platform that has the ability to make financial trades. It computes the reward function based on the loss or profit of every financial transaction.
Reinforcement Learning in NLP (Natural Language Processing)
In NLP, RL can be used in text summarization, question answering, and **machine translation **just to mention a few.
The authors of this paper Eunsol Choi, Daniel Hewlett, and Jakob Uszkoreit propose an RL based approach for question answering given long texts. Their method works by first selecting a few sentences from the document that are relevant for answering the question. A slow RNN is then employed to produce answers to the selected sentences.
#reinforcement learning #deep learning
