  One of the main goals of reinforcement learning (RL) is to provide a~way for
physical machines to learn optimal behavior instead of being programmed.
However, effective control of the machines usually requires 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 our view these
are the main obstacles that prevent application of RL in contemporary control
systems. To address these pitfalls, in this paper we introduce an RL framework
and adequate analytical tools for actions that may be stochastically dependent
in subsequent time instances. We also introduce an RL algorithm that
approximately optimizes a~policy that produces such actions. It applies
experience replay to adjust likelihood of sequences of previous actions to
optimize expected $n$-step returns the policy yields. The efficiency of this
algorithm is verified against four other RL methods (CDAU, 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.

Machine Learning