Top 5 Applications of Reinforcement Learning in Real-Life

Reinforcement learning is the closest thing to human learning. These techniques are the base of all solutions, starting from self-driving cars to surgeons being replaced by medical AI bots. It has become the main driver of emerging technologies and still has plenty to offer to the tech industry. Reinforcement learning applications in various industries are shaping the future of the AI. Here, we have mentioned the top 5 applications of reinforcement learning in real life.

Self-driving Cars – Trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways are examples of autonomous driving tasks where reinforcement learning could be used.

Healthcare – Dynamic treatment regimens (DTRs) in chronic disease or critical care, automated medical diagnosis, and other general domains are applications of RL in healthcare. Reinforcement learning (RL) uses evaluative feedback and interaction experience with the outside world to enhance a person's capacity for behavioral decision-making.

News Recommendation – Because user preferences change frequently, recommending news to users based on reviews and likes may become out of date quickly. The RL system can track the reader's return behaviors using reinforcement learning.

Gaming – One can go from scratch to pro level in a game such as AlphaGo zero by applying reinforcement learning. By delivering a reward signal that specifies high-level goals while allowing the game character to determine the best techniques, reinforcement learning can enable game producers to create considerably more nuanced game characters than traditional methods.

Marketing and Advertising – The authors propose multi-agent reinforcement learning with real-time bidding where a clustering method is used to handle a large number of advertisers, and each cluster is assigned a strategic bidding agent.