Reinforcement Learning for Solving High Complexity Decision-Making Problems

Reinforcement learning (RL) has attracted significant interest in both academia and industry in recent years. The main premise of RL is the ability to control a system efficiently, without requiring any prior knowledge of the dynamics of the system. That being said, using RL as an out of the box approach only works for relatively simple problems with well-defined episodic structures, small number of actions and dense reward signals. On the other hand, many real-world problems possess extremely delayed reward signals, gigantic action spaces and non-episodic dynamics. In this talk, we will show that such high complexity decision making problems can be solved by wrapping RL algorithms with other powerful machine learning techniques, such as curriculum learning, hierarchical decompositions and imitation learning. We will demonstrate the potential of these methods across three different use cases; i) autonomous driving in urban environments, ii) playing real-time strategy games and iii) cloning fighter pilot behavior in air combat.

Online

Lifelong Learning