Robust and Sample Efficient Reinforcement Learning

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Reinforcement Learning (RL) is arguably one of the most comprehensive forms of machine learning. It facilitates active learning and it allows a system to learn over an extended period of time about its environment as it makes a sequence of decisions. The system can also learn from weak signals that might be delayed. This is particularly useful in robotics, autonomous vehicles, conversational agents, game playing, operations research, automated trading, non-myopic recommender systems and self-managing networks. The generality of reinforcement learning also makes it complex and therefore challenging algorithmically. Objectives: The goal of this work is to develop algorithms to improve the robustness and sample efficiency of reinforcement learning. Tremendous progress has been achieved in recent years by deep reinforcement learning techniques that scale to high dimensional inputs (e.g., images and natural language) and complex tasks. However, most of the successes are limited to applications with simulated environments (e.g., games, simulated robotic environments) since current algorithms may execute costly/catastrophic actions and may require an amount of data that is prohibitively large for interaction with real environments. Methods: I will develop novel Bayesian reinforcement learning techniques that can quantify the uncertainty of the environment. This will be helpful both for robustness and sample efficiency. In Bayesian learning, a distribution over the unknowns is estimated and refined at each time step. This distribution also allows a system to explore more efficiently by focusing its actions on the parts of the environment that are still unknown. To that effect, I will develop scalable Bayesian techniques for deep reinforcement learning that explore safely and efficiently. I will also develop novel constrained reinforcement learning techniques that take into account secondary objectives such as variance and cost functions that should not be exceeded. This will further improve robustness by ensuring that key performance indicators (KPIs) are met in industrial applications. I will also develop generative reinforcement learning techniques that are robust to missing inputs. In some applications (e.g., non-myopic recommender systems and self-managing networks), some observations/sensors might not be available at each time step. Generative reinforcement learning techniques that can marginalize inputs in a principled way will be designed.