Advantage policy update based on proximal policy optimization

In this paper, a novel policy network update approach based on Proximal Policy Optimization (PPO), Advantageous Update Policy Proximal Policy Optimization (AUP-PPO), is proposed to alleviate the problem of over-fitting caused by the use of shared layers for policy and value functions. Extended from the previous sample-efficient reinforcement learning method PPO that uses separate networks to learn policy and value functions to make them decouple optimization, AUP-PPO uses the value function to calculate the advantage and updates the policy with the loss between the current and target advantage function as a penalty term instead of the value function. Evaluated by multiple benchmark control tasks in Open-AI gym, AUP-PPO exhibits better generalization to the environment and achieves faster convergence and better robustness compared with the original PPO.