  Actor-critic methods, a type of model-free reinforcement learning (RL), have
achieved state-of-the-art performances in many real-world domains in continuous
control. Despite their success, the wide-scale deployment of these models is
still a far cry. The main problems in these actor-critic methods are
inefficient exploration and sub-optimal policies. Soft Actor-Critic (SAC) and
Twin Delayed Deep Deterministic Policy Gradient (TD3), two cutting edge such
algorithms, suffer from these issues. SAC effectively addressed the problems of
sample complexity and convergence brittleness to hyper-parameters and thus
outperformed all state-of-the-art algorithms including TD3 in harder tasks,
whereas TD3 produced moderate results in all environments. SAC suffers from
inefficient exploration owing to the Gaussian nature of its policy which causes
borderline performance in simpler tasks. In this paper, we introduce
Opportunistic Actor-Critic (OPAC), a novel model-free deep RL algorithm that
employs better exploration policy and lesser variance. OPAC combines some of
the most powerful features of TD3 and SAC and aims to optimize a stochastic
policy in an off-policy way. For calculating the target Q-values, instead of
two critics, OPAC uses three critics and based on the environment complexity,
opportunistically chooses how the target Q-value is computed from the critics'
evaluation. We have systematically evaluated the algorithm on MuJoCo
environments where it achieves state-of-the-art performance and outperforms or
at least equals the performance of TD3 and SAC.
