Variance Reduction for Reinforcement Learning in Input-Driven Environments
This addresses variance reduction in reinforcement learning for applications like queuing systems and robotics, but is incremental as it builds on existing policy gradient methods.
The paper tackles the problem of high variance in reinforcement learning for input-driven environments by deriving an input-dependent baseline and proposing a meta-learning approach to learn it efficiently. Experimental results show improved training stability and better policies across queuing systems, computer networks, and MuJoCo robotic locomotion.
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.