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As the most successful variant and improvement for Trust Region PolicyOptimization (TRPO), proximal policy optimization (PPO) has been widely appliedacross various domains with several advantages: efficient data utilization,easy implementation, and good parallelism. In this paper, a first-ordergradient reinforcement learning algorithm called Policy Optimization withPenalized Point Probability Distance (POP3D), which is a lower bound to thesquare of total variance divergence is proposed as another powerful variant.Firstly, we talk about the shortcomings of several commonly used algorithms, bywhich our method is partly motivated. Secondly, we address to overcome theseshortcomings by applying POP3D. Thirdly, we dive into its mechanism from theperspective of solution manifold. Finally, we make quantitative comparisonsamong several state-of-the-art algorithms based on common benchmarks.Simulation results show that POP3D is highly competitive compared with PPO.Besides, our code is released in https://github.com/paperwithcode/pop3d.

Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization