  Model-free deep reinforcement learning (RL) algorithms have been demonstrated
on a range of challenging decision making and control tasks. However, these
methods typically suffer from two major challenges: very high sample complexity
and brittle convergence properties, which necessitate meticulous hyperparameter
tuning. Both of these challenges severely limit the applicability of such
methods to complex, real-world domains. In this paper, we propose soft
actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum
entropy reinforcement learning framework. In this framework, the actor aims to
maximize expected reward while also maximizing entropy. That is, to succeed at
the task while acting as randomly as possible. Prior deep RL methods based on
this framework have been formulated as Q-learning methods. By combining
off-policy updates with a stable stochastic actor-critic formulation, our
method achieves state-of-the-art performance on a range of continuous control
benchmark tasks, outperforming prior on-policy and off-policy methods.
Furthermore, we demonstrate that, in contrast to other off-policy algorithms,
our approach is very stable, achieving very similar performance across
different random seeds.
