  Off-policy learning is more unstable compared to on-policy learning in
reinforcement learning (RL). One reason for the instability of off-policy
learning is a discrepancy between the target ($\pi$) and behavior (b) policy
distributions. The discrepancy between $\pi$ and b distributions can be
alleviated by employing a smooth variant of the importance sampling (IS), such
as the relative importance sampling (RIS). RIS has parameter $\beta\in[0, 1]$
which controls smoothness. To cope with instability, we present the first
relative importance sampling-off-policy actor-critic (RIS-Off-PAC) model-free
algorithms in RL. In our method, the network yields a target policy (the
actor), a value function (the critic) assessing the current policy ($\pi$)
using samples drawn from behavior policy. We use action value generated from
the behavior policy in reward function to train our algorithm rather than from
the target policy. We also use deep neural networks to train both actor and
critic. We evaluated our algorithm on a number of Open AI Gym benchmark
problems and demonstrate better or comparable performance to several
state-of-the-art RL baselines.
