Reinforcement Learning
Reinforcement Learning (RL) is a type of machine learning in which an autonomous agent learns to make sequential decisions by interacting with an environment. Through trial and error, the agent receives feedback in the form of rewards or penalties, allowing it to improve its strategy over time to maximize long-term outcomes. Unlike supervised learning, RL does not rely on labeled datasets—instead, it discovers optimal behaviors by exploring and evaluating the consequences of its actions.

At NEC Labs America, reinforcement learning is being applied to cutting-edge domains such as real-time camera optimization, drug discovery, and industrial efficiency—often in combination with imitation learning, adversarial learning, and other hybrid approaches to solve complex, real-world challenges.

Read our reinforcement learning publications below.

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Learning To Simulate

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Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.

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