This book presents and develops new reinforcement learning methods
that enable fast and robust learning on robots in real-time. Robots
have the potential to solve many problems in society, because of
their ability to work in dangerous places doing necessary jobs that
no one wants or is able to do. One barrier to their widespread
deployment is that they are mainly limited to tasks where it is
possible to hand-program behaviors for every situation that may be
encountered. For robots to meet their potential, they need methods
that enable them to learn and adapt to novel situations that they
were not programmed for. Reinforcement learning (RL) is a paradigm
for learning sequential decision making processes and could solve
the problems of learning and adaptation on robots. This book
identifies four key challenges that must be addressed for an RL
algorithm to be practical for robotic control tasks. These RL for
Robotics Challenges are: 1) it must learn in very few samples; 2)
it must learn in domains with continuous state features; 3) it must
handle sensor and/or actuator delays; and 4) it should continually
select actions in real time. This book focuses on addressing all
four of these challenges. In particular, this book is focused on
time-constrained domains where the first challenge is critically
important. In these domains, the agent’s lifetime is not long
enough for it to explore the domains thoroughly, and it must learn
in very few samples.
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