Learning Control Policies for Variable Objectives from Offline Data
Marc Weber Phillip Swazinna Daniel Hein Steffen Udluft Volkmar Sterzing
Offline reinforcement learning provides a viable approach to obtain advanced
control strategies for dynamical systems, in particular when direct interaction
with the environment is not available. In this paper, we introduce a conceptual
extension for model-based policy search methods, called variable objective
policy (VOP). With this approach, policies are trained to generalize
efficiently over a variety of objectives, which parameterize the reward
function. We demonstrate that by altering the objectives passed as input to the
policy, users gain the freedom to adjust its behavior or re-balance
optimization targets at runtime, without need for collecting additional
observation batches or re-training.