Experienced Machine Learning Engineer and Reinforcement Learning Researcher with a strong background in conducting hands-on data analysis, building large-scale deep learning models and pipelines. Experience designing, developing, and deploying deep learning solutions.
User preferences can change frequently, therefore recommending news to users based on reviews and likes could become obsolete quickly. With reinforcement learning, the RL system can track the reader’s return behaviors.
Deep Learning is applied in almost all fields. Thus, this method is frequently termed as a universal learning method. DL is being used in numerous situations where machine intelligence can be beneficial such as navigation on Mars where there is the absence of a human expert, vision, speech recognition, and language understanding and biometrics, personalization for solutions in particular cases.
In NLP, Reinforcement Learning can be used in text summarization, question answering, and machine translation. Researchers from Stanford University, Ohio State University, and Microsoft Research have fronted Deep RL for use in dialogue generation.
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Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI (Artificial Intelligence) will transform in the next several years.
In the long run, curiosity-driven research just works better...
Real breakthroughs come from people focusing on what they're excited about.
Reinforcement learning is learning what to do—how to map situations to actions—so as to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them.
Flashlight is a new open source machine learning (ML) library, written entirely in C++, that was built by FAIR to power groundbreaking research by enabling teams to rapidly and easily modify deep and ML frameworks to better fit their needs. sFinding the right code to change is time-consuming and error-prone, as low-level internals can be unintentionally obfuscated, closed-source, or hand-tuned for particular purposes.
mbrl-lib is a toolbox for facilitating development of Model-Based Reinforcement Learning algorithms. It provides easily interchangeable modeling and planning components, and a set of utility functions that allow writing model-based RL algorithms with only a few lines of code.
Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences, and thus an agent can extract information about what humans want from the state.
