  Deep Reinforcement Learning (DRL) has shown outstanding performance on
inducing effective action policies that maximize expected long-term return on
many complex tasks. Much of DRL work has been focused on sequences of events
with discrete time steps and ignores the irregular time intervals between
consecutive events. Given that in many real-world domains, data often consists
of temporal sequences with irregular time intervals, and it is important to
consider the time intervals between temporal events to capture latent
progressive patterns of states. In this work, we present a general Time-Aware
RL framework: Time-aware Q-Networks (TQN), which takes into account physical
time intervals within a deep RL framework. TQN deals with time irregularity
from two aspects: 1) elapsed time in the past and an expected next observation
time for time-aware state approximation, and 2) action time window for the
future for time-aware discounting of rewards. Experimental results show that by
capturing the underlying structures in the sequences with time irregularities
from both aspects, TQNs significantly outperform DQN in four types of contexts
with irregular time intervals. More specifically, our results show that in
classic RL tasks such as CartPole and MountainCar and Atari benchmark with
randomly segmented time intervals, time-aware discounting alone is more
important while in the real-world tasks such as nuclear reactor operation and
septic patient treatment with intrinsic time intervals, both time-aware state
and time-aware discounting are crucial. Moreover, to improve the agent's
learning capacity, we explored three boosting methods: Double networks, Dueling
networks, and Prioritized Experience Replay, and our results show that for the
two real-world tasks, combining all three boosting methods with TQN is
especially effective.
