  The subject of this paper is reinforcement learning. Policies are considered
here that produce actions based on states and random elements autocorrelated in
subsequent time instants. Consequently, an agent learns from experiments that
are distributed over time and potentially give better clues to policy
improvement. Also, physical implementation of such policies, e.g. in robotics,
is less problematic, as it avoids making robots shake. This is in opposition to
most RL algorithms which add white noise to control causing unwanted shaking of
the robots. An algorithm is introduced here that approximately optimizes the
aforementioned policy. Its efficiency is verified for four simulated learning
control problems (Ant, HalfCheetah, Hopper, and Walker2D) against three other
methods (PPO, SAC, ACER). The algorithm outperforms others in three of these
problems.
