Since RL requires an agent and an environment to interact with each other, the first example that may spring to mind is the earth, the physical world we live in. Unfortunately, for now, it is actually used in only a few cases. With the current algorithms, the problems stem from the large number of interactions that an agent has to execute with the environment in order to learn good behaviors. It may require hundreds, thousands, or even millions of actions, requiring way too much time to be feasible. One solution is to use simulated environments to start the learning process and, only at the end, fine-tune it in the real world. This approach is way better than learning just from the world around it, but still requires slow real-world interactions. However, in many cases, the task can be fully simulated. To research and implement RL algorithms, games, video...
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You're reading from Reinforcement Learning Algorithms with Python
Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
Read more about Andrea Lonza
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Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
Read more about Andrea Lonza