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In recent years, reinforcement learning (RL) has gained increasing attentionin control engineering. Especially, policy gradient methods are widely used. Inthis work, we improve the tracking performance of proximal policy optimization(PPO) for arbitrary reference signals by incorporating information about futurereference values. Two variants of extending the argument of the actor and thecritic taking future reference values into account are presented. In the firstvariant, global future reference values are added to the argument. For thesecond variant, a novel kind of residual space with future reference valuesapplicable to model-free reinforcement learning is introduced. Our approach isevaluated against a PI controller on a simple drive train model. We expect ourmethod to generalize to arbitrary references better than previous approaches,pointing towards the applicability of RL to control real systems.

Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information