Reinforcement learning is an area of machine learning where so-called agents are used to find optimal paths to complete certain tasks. Agents try to behave like humans on a single task. Agent receives an input (Observations about the environment e.g. pictures from a camera) and produces output (Action e.g. “turn right”). In order to achieve optimal actions to certain inputs, agents are introduced a reward function (e.g. Do this as soon as possible and receive a reward). After specifying the environment and rewards, agents need to be trained in order for them to learn the task. In contrast to supervised learning, reinforcement learning does not use labeled data, but learns from interacting with given environment.
Simulation environments are becoming more realistic and it starts to be more evident that work machine prototyping, development and also automation can be developed in simulation environments to a very mature state. This speeds up product development lead time and also saves cost in prototyping. The following 2 videos showcase our contribution to the AI4DI project.
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