Reinforcement learning (RL) algorithms commonly provide a one-action plan per time step. Doing this allows the RL agent to quickly adapt and respond to stochastic environments yet it restricts the ability to predict the agent's future behavior. This paper proposes an actor-critic framework that predicts and follows an n-step plan. Committing to the next n actions presents a trade-off between behavior predictability and reduced performance. In order to balance this trade-off, a dynamic plan-follo…