Reinforcement learning (RL) is an important area of machine learning inspired by behaviourist psychology, concerned with an agent learn how to behave in a environment by performing actions so as to maximize some notion of cumulative reward. Inception Institute focuses on reinforcement learning including both policy-search and temporal-difference based, as well as closely related methods for sample-based decision theoretic planning. Our efforts will be fully-dedicated to develop AI agents which are able to learn in an interactive environment by exploring solutions and automatically determine the ideal behaviour. Basically, the AI agents operate by getting a feedback information from trial and error using its own actions and experiences. For RL research, the techniques such as Monte Carlo tree search, swarm intelligence, control theory, operations research, information theory, optimisation theory and deep learning are expected to play key roles. Additionally, we apply RL in some real-word applications including automatic natural language processing, speech analysis, healthcare, image processing, video analytics.