{"id":33609,"date":"2023-10-10T03:00:00","date_gmt":"2023-10-10T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33609"},"modified":"2023-10-09T17:23:03","modified_gmt":"2023-10-10T00:23:03","slug":"reinforcement-learning-balancing-exploration-and-exploitation","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/10\/10\/reinforcement-learning-balancing-exploration-and-exploitation\/","title":{"rendered":"Reinforcement Learning: Balancing Exploration and Exploitation"},"content":{"rendered":"\n

For those unfamiliar, Reinforcement Learning (RL) is the science of decision making, specifically referring to how certain behaviors are encouraged, and others discouraged. It is one of three approaches to machine learning (ML) and it has the potential to yield simple solutions to complex problems using a systematic, rewards-based method within a particular environment to ensure the greatest reward. But how does RL differ from other methods?<\/p>\n\n\n\n

Unlike unsupervised approaches to ML – where an algorithm analyzes a large dataset to find clusters or patterns or, supervised approaches, which teach a model by giving it the right answers – reinforcement learning is a method that trains a \u201cmachine\u201d to make a sequence of decisions to learn about its environment and assess opportunities for the greatest reward. <\/p>\n\n\n\n

Here, the agent with the model decides to make certain actions based on both the environment\u2019s current state and all the related known history. The environment then naturally responds to those actions. Once complete, the agent then observes those responses and assigns a certain reward to them. Essentially, the goal of the reinforcement learning model is to ascertain the best action that the machine can conduct to give the highest reward within the environmental state. Put simply, RL is an iterative process that involves continuous learning to reach the optimal result.<\/p>\n\n\n\n

Reinforcement Learning in Real Life<\/strong><\/p>\n\n\n\n

Let\u2019s look at how RL is being used today. When a robotic vacuum cleaner maneuvers around a new environment and  hits a wall or obstacle, it responds by re-routing to a clear path. Once the  internal mapping has been completed, the reward is the new ability to  navigate the room without bumps or blocks in the future, leading to a clean surface with a minimum of effort.<\/p>\n\n\n\n

Or consider a self-driving car to further illustrate the capabilities of  RL. In this example, the environment is the road, the lines, the lights, the cars and the agent is the brain of the car. It is critical to ensure that the vehicle can navigate routes, find and park in available spots, and change lanes. RL allows the vehicle to gather information about its environment and be rewarded for good behavior by way of successfully fulfilling its tasks. Conversely, a negative reward is given for taking the wrong action. Self-driving cars are loaded with data regarding the road mapping, existing laws, and preferred routes, and will develop learned behavior over time using RL. <\/p>\n\n\n\n

The autonomous vehicle will use two systems to gather additional information and seek rewards: <\/p>\n\n\n\n