Model-Based Reinforcement Learning via Proximal Policy Optimization

Abstract

Proximal policy optimization (PPO) is the state-of the-art most effective model-free reinforcement learning algorithm. Its powerful policy search ability allows an agent to find the optimal policy by trial and error but leads to high computation and low data-efficiency. Model-based algorithms can make the most efficient use of data by learning a forward model from observation, but face the challenge of model error. In this paper, we combine the strengths of both algorithms and introduce a data-efficient model-based approach called PIPPO (probabilistic inference via PPO). It makes online probabilistic dynamic model inference based on Gaussian process regression and executes offline policy improvement using PPO on the inferred model. Empirical evaluation on the pendulum benchmark problem shows that the proposed PIPPO algorithm has comparable performance and less interaction with the environment compared with traditional PPO.