  We consider reinforcement learning in input-driven environments, where an
exogenous, stochastic input process affects the dynamics of the system. Input
processes arise in many applications, including queuing systems, robotics
control with disturbances, and object tracking. Since the state dynamics and
rewards depend on the input process, the state alone provides limited
information for the expected future returns. Therefore, policy gradient methods
with standard state-dependent baselines suffer high variance during training.
We derive a bias-free, input-dependent baseline to reduce this variance, and
analytically show its benefits over state-dependent baselines. We then propose
a meta-learning approach to overcome the complexity of learning a baseline that
depends on a long sequence of inputs. Our experimental results show that across
environments from queuing systems, computer networks, and MuJoCo robotic
locomotion, input-dependent baselines consistently improve training stability
and result in better eventual policies.
