Deep Reinforcement LearningDeep reinforcement learning (DRL) has achieved groundbreaking success for many decision making problems, both in discrete and continuous domains, including robotics, game playing, and many others. In reinforcement learning, an agent does not have access to the transition dynamics and reward functions of the environment. Therefore, it learns an optimal policy by interacting with the unknown environment, which can suffer from high sample complexity and limited applications in high-dimensional real-world problems. Therefore, it is important for the agent to find efficient and practical ways for learning. In this research thrust, we are interested in (i) designing efficient, practical DRL algorithms in specific domains, and (ii) improving the sample efficiency of RL algorithms for general tasks. Recent publications:
Online LearningExisting online learning framework focuses on learning parameters via a series of feedback in an ideal situation. However, real-world applications can be much more complicated, and there are still many challenges in making the model close to reality. For example, in some online systems, instead of the system itself, users make decisions on what actions to choose. In this case, it remains unsolved how to influence users, so that the system learns from the feedback efficiently. As another example, in many cases, action feedback can have delay and maybe convoluted with prior action feedback. How to optimally learn and control remains largely open. In this thrust, we are interested in designing efficient algorithms for the online learning problem under real-world constraints, and extending existing online learning results. Recent publications:
Stochastic Optimization and Machine LearningEfficient algorithms are key to machine learning and data science. It is critical to design algorithms that have probable performance guarantees and fast convergence. Moreover, in many practical settings, it is also important to design algorithms that allow distributed implementation and are robust to communication errors or delay. In this thrust, we are interested in designing efficient distributed algorithms for both convex and non-convex optimization problems. Recent publications:
Learning-aided Stochastic Network OptimizationExisting network optimization results have mostly been focusing on designing algorithms either based on full a-priori statistical knowledge of the system, or based on stochastic approximation for systems with zero knowledge beforehand. These two scenarios, though being very general, do not explicitly capture the role of information learning in control and do not reap the potential benefits of it. This ignorance often leads to a mismatch between algorithms in the literature and practical control schemes, where system information (data) is often constantly collected and incorporated into operations. In this research thrust, we are interested in (i) quantifying fundamental benefits and limits of learning in network optimization, and (ii) designing simple and practical algorithms for achieving the full benefits. Recent publications:
Predictive Control in Information SystemsThe rapid development of machine learning and user behavior study have made it possible to learn and predict user behavior, e.g., mobility patterns, user preferences and software resource demand. Various predictive control schemes have also been implemented in practice to significantly improve user experience. Despite such success, there has been limited theoretical understanding about how prediction fundamentally impacts system performance. Moreover, it is not clear how prediction can be efficiently incorporated into control algorithm design. In this thrust, we are interested in establishing a general framework for studying the impact of prediction and predictive algorithm design, with the objective of further improving the performance of causal algorithms. Recent publications:
Sharing Economy: Welfare and RevenueSharing economy has emerged as an enabling method for efficiently utilizing social resources that will otherwise have low-utilization. However, the growth of the sharing economy is driven mainly by sharing platforms, whose objectives might not be exactly aligned with social welfare. Thus, many interesting and important questions remain unclear. For instance, how much welfare loss may occur due to the mis-alignment of platform objective, how do prices and subsidies impact system performance, and how do we design optimal loyalty programs. In this research direction, we are interested in fundamental questions regarding social welfare, platform management, and incentive mechanisms. Recent publications:
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