Title: PGPS : Coupling Policy Gradient with Population-based Search. Abstract: Gradient-based policy search algorithms (such as PPO, SAC or TD3) in deep reinforcement learning (DRL) have shown successful results on a range of challenging control tasks. However, they often suffer from flat or deceptive gradient problems. As an alternative to policy gradient methods, population-based evolutionary approaches have been applied to DRL. While population-based search algorithms show more robust learning in a broader range of tasks, they are usually inefficient in the use of samples. Recently, reported are a few attempts (such as CEMRL) to combine gradient with a population in searching optimal policy. This kind of hybrid algorithm takes advantage of both camps. In this paper, we propose yet another hybrid algorithm, which more tightly couples policy gradient with the population-based search. More specifically, we use the Cross-Entropy Method (CEM) for population-based search and Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy gradient. In the proposed algorithm called Coupling Policy Gradient with Population-based Search (PGPS), a single TD3 agent, which learns by a gradient from all experiences generated by population, leads a population by providing its critic function Q as a surrogate to select better performing next-generation population from candidates. On the other hand, if the TD3 agent falls behind the CEM population, then the TD3 agent is updated toward the elite member of the CEM population using loss function augmented with the distance between the TD3 and the CEM elite. Experiments in a MuJoCo environment show that PGPS is robust to deceptive gradient and also outperforms the state-of-the-art algorithms.
