  The relationship between a reinforcement learning (RL) agent and an
asynchronous environment is often ignored. Frequently used models of the
interaction between an agent and its environment, such as Markov Decision
Processes (MDP) or Semi-Markov Decision Processes (SMDP), do not capture the
fact that, in an asynchronous environment, the state of the environment may
change during computation performed by the agent. In an asynchronous
environment, minimizing reaction time---the time it takes for an agent to react
to an observation---also minimizes the time in which the state of the
environment may change following observation. In many environments, the
reaction time of an agent directly impacts task performance by permitting the
environment to transition into either an undesirable terminal state or a state
where performing the chosen action is inappropriate. We propose a class of
reactive reinforcement learning algorithms that address this problem of
asynchronous environments by immediately acting after observing new state
information. We compare a reactive SARSA learning algorithm with the
conventional SARSA learning algorithm on two asynchronous robotic tasks
(emergency stopping and impact prevention), and show that the reactive RL
algorithm reduces the reaction time of the agent by approximately the duration
of the algorithm's learning update. This new class of reactive algorithms may
facilitate safer control and faster decision making without any change to
standard learning guarantees.
