… Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary
environment. However, the stationary assumption on the environment is very restrictive. In …

… any real-life situation, is often constrained to make the problem solvable. RL practitioners
create an interface to interact with the environment. This could be a simulation, real life, or a …

… of reinforcement learning. It is … reinforcement learning requires reinforcement learning
agents to be embedded into the flow of real-world experience, where they act, explore, and learn …

… Please refer to [9] for a review of imitation learning methods. In real life, a human demonstration
is usually not perfect nor does it suffice for near-optimal performance. Thus, additional …

… This paper explores the potential societal implications of one avenue of AI research currently
showing promise: Deep Reinforcement Learning (DRL). The paper is primarily aimed at …

… reinforcement learning that will reappear through the rest of the book. We also implement our
first practical reinforcement learning … started with a real reinforcement learning problem and …

… reinforcement learning in systems with unknown dynamics. Algorithms such as E" (Kearns
and Singh, 2002) learn … In this paper, we consider the apprenticeship learning setting in which …

… A critical trait of a robot AI is the ability to dream or in other words to simulate its behavior
as humans do and to learn from it for real life. Popular robot simulation environments like …

… Reinforcement learning (RL) is one of the most remarkable branches of machine learning
and … to examine essential aspects of reinforcement learning in machine scheduling problems, …

… Section V discusses the main conclusions and future work towards reallife learning for … of
real-life learning for multi-robot systems. One development would be to improve model learning …