Paper:
TRUST-PCL: AN OFF-POLICY TRUST REGION METHOD FOR CONTINUOUS CONTROL
Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL). While current trust region strategies are effective for continuous control, they typically require a large amount of on-policy interaction with the environment. To address this problem, we propose an off-policy trust region method, Trust-PCL, which exploits an observation that the optimal policy and state values of a maximum reward objective with a relative-entropy regularizer satisfy a set of multi-step pathwise consistencies along any path. The introduction of relative entropy regularization allows Trust-PCL to maintain optimization stability while exploiting off-policy data to improve sample efficiency. When evaluated on a number of continuous control tasks, Trust-PCL significantly improves the solution quality and sample efficiency of TRPO.

Review:
This paper presents a policy gradient method that employs entropy regularization and entropy constraint at the same time. The entropy regularization on action probability is to encourage the exploration of the policy, while the entropy constraint is to stabilize the gradient.
The major weakness of this paper is the unclear presentation. For example, the algorithm is never fully described, though a handful variants are discussed. How the off-policy version is implemented is missing.
In experiments, why the off-policy version of TRPO is not compared. Comparing the on-policy results, PCL does not show a significant advantage over TRPO. Moreover, the curves of TRPO is so unstable, which is a bit uncommon. 
What is the exploration strategy in the experiments? I guess it was softmax probability. However, in many cases, softmax does not perform a good exploration, even if the entropy regularization is added.
Another issue is the discussion of the entropy regularization in the objective function. This regularization, while helping exploration, do changes the original objective. When a policy is required to pass through a very narrow tunnel of states, the regularization that forces a wide action distribution could not have a good performance. Thus it would be more interesting to see experiments on more complex benchmark problems like humanoids.