ACERAC: Efficient reinforcement learning in fine time discretization

We propose a framework for reinforcement learning (RL) in fine time discretization and a learning algorithm in this framework. One of the main goals of RL is to provide a way for physical machines to learn optimal behavior instead of being programmed. However, the machines are usually controlled in fine time discretization. The most common RL methods apply independent random elements to each action, which is not suitable in that setting. It is not feasible because it causes the controlled system to jerk, and does not ensure sufficient exploration since a single action is not long enough to create a significant experience that could be translated into policy improvement. In the RL framework introduced in this paper, policies are considered that produce actions based on states and random elements autocorrelated in subsequent time instants. The RL algorithm introduced here approximately optimizes such a policy. The efficiency of this algorithm is verified against three other RL methods (PPO, SAC, ACER) in four simulated learning control problems (Ant, HalfCheetah, Hopper, and Walker2D) in diverse time discretization. The algorithm introduced here outperforms the competitors in most cases considered.